In 2005, National Geo-spatial Information noticed a global trend towards digital image acquisition and decided to invest in a digital camera (an Intergraph DMC). Since 2008, all images have been captured digitally with this camera.This meant that the acquisition of the traditional photo-scale had now been replaced with a Ground Sample Distance (GSD). The ground sample distance is the size of 1 pixel on the ground and is influenced by the flying height and focal length.
Currently, 12-bit images are captured in RGB (true colour), Near infra-red and Panchromatic, with a GSD of 0.5m They are stored as Tiff files with a JPEG compression of Q=3; with tiles and a full set of overviews. Each DMC image is approximately 7km by 3.8km and they are mosaiced together to fit the 1:10 000 reference sheets after orthorectification. The aim is to capture 40% of the country every 3 years and the remaining areas every 5 years.
The current dataset displays imagery captured between 2008 and 2012 and should only be used for reference purposes.
This dataset represents above- and below-ground terrestrial carbon\nstorage (tonnes (t) of C per hectare (ha)) for circa 2010.
This layer supports analysis but, if needed, a direct download of the data can be accessed here.
The dataset was constructed by combining the most reliable publicly\navailable datasets and overlying them with the ESA CCI landcover map for the\nyear 2010 [ESA, 2017], assigning to each grid cell the corresponding\nabove-ground biomass value from the biomass map that was most appropriate for\nthe grid cell’s landcover type.
Input carbon datasets were identified through a literature review of\nexisting datasets on biomass carbon in terrestrial ecosystems published in\npeer-reviewed literature. To determine which datasets to combine to produce the\nglobal carbon density map, identified datasets were evaluated based on\nresolution, accuracy, biomass definition and reference date (see table 1 for\nfurther information on datasets selected).
| \n Dataset \n | \n \n Scope \n | \n \n Year \n | \n \n Resolution \n | \n \n Definition \n | \n
| \n Santoro et al. 2018 \n | \n \n Global \n | \n \n 2010 \n | \n \n 100 m \n | \n \n Above-ground woody biomass for trees\n that are >10 cm diameter-at-breast-height, masked to Landsat-derived\n canopy cover for 2010; biomass is expressed as oven-dry weight of the woody\n parts (stem, bark, branches and twigs) of all living trees excluding stump\n and roots. \n | \n
| \n Xia et al. 2014 \n | \n \n Global \n | \n \n 1982-2006 \n | \n \n 8 km \n | \n \n Above-ground grassland biomass. \n | \n
| \n Bouvet et al. 2018 \n | \n \n Africa \n | \n \n 2010 \n | \n \n 25 m \n | \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts. \n | \n
| \n Spawn et al. 2017 \n | \n \n Global \n | \n \n 2010 \n | \n \n 300 m \n | \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks. \n | \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
Below-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
", "keyword": [ "unep-wcmc", "biomass", "carbon" ], "issued": "2021-05-04T14:55:46.000Z", "modified": "2021-12-08T15:43:25.000Z", "publisher": { "name": "UN Environment World Conservation Monitoring Centre" }, "contactPoint": { "@type": "vcard:Contact", "fn": "UNEP-WCMC-Publisher", "hasEmail": "mailto: info@unep-wcmc.org" }, "accessLevel": "public", "spatial": "-180.0000,-56.0035,180.0000,84.0000", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::above-and-below-ground-biomass-carbon-and-soil-organic-carbon-to-1m-depth-tonnes-ha" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://data-gis.unep-wcmc.org/portal/sharing/servers/374a99fc76574f72bb8c71af7b428d0a/rest/services/World_Biomass_and_Soil_Carbon/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=73d3b47c9305483f9b7866aed1bcd3e5", "landingPage": "https://uneca.africageoportal.com/apps/CenterForGIS::kazava-project", "title": "KAZAVA Project", "description": "This dataset represents above- and below-ground terrestrial carbon\nstorage (tonnes (t) of C per hectare (ha)) for circa 2010.
This layer supports analysis but, if needed, a direct download of the data can be accessed here.
The dataset was constructed by combining the most reliable publicly\navailable datasets and overlying them with the ESA CCI landcover map for the\nyear 2010 [ESA, 2017], assigning to each grid cell the corresponding\nabove-ground biomass value from the biomass map that was most appropriate for\nthe grid cell’s landcover type.
Input carbon datasets were identified through a literature review of\nexisting datasets on biomass carbon in terrestrial ecosystems published in\npeer-reviewed literature. To determine which datasets to combine to produce the\nglobal carbon density map, identified datasets were evaluated based on\nresolution, accuracy, biomass definition and reference date (see table 1 for\nfurther information on datasets selected).
| \n Dataset \n | \n \n Scope \n | \n \n Year \n | \n \n Resolution \n | \n \n Definition \n | \n
| \n Santoro et al. 2018 \n | \n \n Global \n | \n \n 2010 \n | \n \n 100 m \n | \n \n Above-ground woody biomass for trees\n that are >10 cm diameter-at-breast-height, masked to Landsat-derived\n canopy cover for 2010; biomass is expressed as oven-dry weight of the woody\n parts (stem, bark, branches and twigs) of all living trees excluding stump\n and roots. \n | \n
| \n Xia et al. 2014 \n | \n \n Global \n | \n \n 1982-2006 \n | \n \n 8 km \n | \n \n Above-ground grassland biomass. \n | \n
| \n Bouvet et al. 2018 \n | \n \n Africa \n | \n \n 2010 \n | \n \n 25 m \n | \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts. \n | \n
| \n Spawn et al. 2017 \n | \n \n Global \n | \n \n 2010 \n | \n \n 300 m \n | \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks. \n | \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
Below-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
This dataset has not been validated.
", "keyword": [ "biomass", "carbon", "unep-wcmc" ], "issued": "2021-05-04T15:00:16.000Z", "modified": "2021-05-10T15:23:24.000Z", "publisher": { "name": "UN Environment World Conservation Monitoring Centre" }, "contactPoint": { "@type": "vcard:Contact", "fn": "UNEP-WCMC-Publisher", "hasEmail": "mailto: info@unep-wcmc.org" }, "accessLevel": "public", "spatial": "-180.0000,-62.8496,180.0000,89.0000", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::above-and-below-ground-biomass-carbon-tonnes-ha" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://data-gis.unep-wcmc.org/portal/sharing/servers/8a8d4e24683a46e6b039aea78c8af20f/rest/services/World_Biomass_Carbon/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=c989582ced0445aebfcb093f271fa452", "landingPage": "https://uneca.africageoportal.com/apps/africageoportal::locust-in-africa-time-enabled-1", "title": "Locust in Africa Time Enabled", "description": "", "keyword": [ "Locusts", "Africa", "Desert Locusts", "Food Security", "Agriculture" ], "issued": "2021-05-03T16:25:48.000Z", "modified": "2021-05-03T17:49:34.000Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "okeyopauline", "hasEmail": "mailto: africageoportal@esri.com" }, "accessLevel": "public", "spatial": "-26.1169,-6.1279,52.2037,37.8165", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/africageoportal::locust-in-africa-time-enabled-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://africageoportal.maps.arcgis.com/apps/instant/media/index.html?appid=c989582ced0445aebfcb093f271fa452" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=7fedaa3e3a654cda826b01448b4009b0", "landingPage": "https://uneca.africageoportal.com/apps/ageos-tunisie::communes-selon-les-5-catégories-de-populations-beta", "title": "Communes selon les 5 catégories de Populations (beta)", "description": "Communes selon les 5 catégories de Populations (Source INS Janvier 2020)", "keyword": [ "communes", "municipalités", "populations", "tunisie", "tunisia" ], "issued": "2020-12-10T21:32:43.000Z", "modified": "2021-01-15T14:47:26.000Z", "publisher": { "name": "AGEOS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "ageos.admins", "hasEmail": "mailto:info@ageos.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/ageos-tunisie::communes-selon-les-5-catégories-de-populations-beta" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=c87bf2864a1d4e639e89b09fda6df247", "landingPage": "https://uneca.africageoportal.com/maps/tunisia::ruines-de-carthage-tunisia-by-graphtech-", "title": "Ruines de Carthage (Tunisia) by Graphtech.", "description": "According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.
In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite.
Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.
We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction.
For access to the original data, see https://search.datacite.org/works/10.15493/sarva.301020-2.
SOURCES
Wet and Wonderful: The World's Largest Wetlands Are Conservation Priorities, Paul A. Keddy et al, BioScience, January 2009
The World's Largest Wetlands: Ecology and Conservation, edited by Lauchlan H. Fraser and Paul A. Keddy, Cambridge University Press, 2005
The United States Geological Survey has published "An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems" in Global Ecology and Conservation Journal. This work was produced by a team led by Roger Sayre, Ph.D., Senior Scientist for Ecosytems at the USGS Land Change Science Program with the support from The Nature Conservancy and Esri.
We described this work using two introduction story maps, Introduction to World Ecosystems Map and Introduction to World Climate Regions Map. This story map is an introduction for World Climate Regions Map. You can have more information by accessing the published paper and you can access the dataset by downloading the pro package.
This work is licensed under the Esri Master License Agreement.", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/aa88482528574b06a17927ece1b48892" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=fe4e7d6e46254070a612fd99a8854e4a", "landingPage": "https://uneca.africageoportal.com/maps/fe4e7d6e46254070a612fd99a8854e4a", "title": "Routes du Maroc", "description": "
La longueur totale du réseau routier classé est de 57 334 km, dont 43 318 km revêtu, soit 75% de la longueur du réseau classé.
Les autoroutes totalise un linéaire de 1 588 km tandis que le réseau routier revêtu (Hors agglomération) est réparti comme suit :
Routes Nationales : 10.203 km.Routes Régionales : 9.370 km.Routes Provinciales : 23.745 km.This web map uses the World Shaded Relief and World Reference Overlay map services.
", "keyword": [ "Côte d'Ivoire", "relief", "terrain", "topographie", "soils", "geology", "landscape" ], "issued": "2012-07-05T15:27:09.000Z", "modified": "2018-03-30T11:56:51.000Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "Esri_France_Content_AFR", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-11.7534,3.3280,2.4410,11.6317", "license": "
This work is licensed under the Web Services and API Terms of Use. Cette application\r\nWeb va vous permettre de visualiser la population dans l’ensemble des\r\ndélégations du gouvernorat de Tunis en 2009. En cliquant sur les différentes entités\r\nvous découvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/2e2f0fcd954744369d2b11fa52fed42e" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/Legend/main/index.html?appid=2e2f0fcd954744369d2b11fa52fed42e" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=2a01ce7917e6452e81506bcbbd21000b", "landingPage": "https://uneca.africageoportal.com/apps/2a01ce7917e6452e81506bcbbd21000b", "title": "Population par délégation (Manouba 2009)", "description": "\r\n\r\nCette application\r\nWeb va vous permettre de visualiser la population dans l’ensemble des\r\ndélégations du gouvernorat de Manouba en 2009. En cliquant sur les différentes entités\r\nvous découvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/2a01ce7917e6452e81506bcbbd21000b" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/Legend/main/index.html?appid=2a01ce7917e6452e81506bcbbd21000b" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=40471f47754649b0a7a636f6208fdb5e", "landingPage": "https://uneca.africageoportal.com/apps/40471f47754649b0a7a636f6208fdb5e", "title": "Population par délégation (Ariana 2009)", "description": "\r\n\r\nCette application\r\nWeb va vous permettre de visualiser la population dans l’ensemble des\r\ndélégations du gouvernorat de l’Ariana en 2009. En cliquant sur les différentes\r\nentités vous découvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/40471f47754649b0a7a636f6208fdb5e" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/Legend/main/index.html?appid=40471f47754649b0a7a636f6208fdb5e" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=3119f3267b8249e6a73e743b0dd84f53", "landingPage": "https://uneca.africageoportal.com/apps/3119f3267b8249e6a73e743b0dd84f53", "title": "Traffic routier de Manouba (2007) - road traffic", "description": "\r\n\r\nCette application\r\nWeb va vous permettre de visualiser le traffic routier dans le gouvernorat de Manouba\r\nen 2007. En cliquant sur les différentes entités vous découvrirez les\r\ninformations liées.
\r\n\r\nSource : INS Tunisia (2007).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/3119f3267b8249e6a73e743b0dd84f53" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=3119f3267b8249e6a73e743b0dd84f53" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4b8a1d98c9c94a98a6502ab0fc9d004b", "landingPage": "https://uneca.africageoportal.com/apps/4b8a1d98c9c94a98a6502ab0fc9d004b", "title": "Carte Occupation du Sol (Ariana 2011) - Soil cover map", "description": "\r\n\r\nCette application\r\nWeb va vous permettre de visualiser la desserte en eau potable dans le\r\ngouvernorat de l’Ariana. En cliquant sur les différentes entités vous\r\ndécouvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2011).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/4b8a1d98c9c94a98a6502ab0fc9d004b" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/TwoPane/main/index.html?appid=4b8a1d98c9c94a98a6502ab0fc9d004b" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=fd975f1436e94dfc8f2201cc961deb3c", "landingPage": "https://uneca.africageoportal.com/apps/fd975f1436e94dfc8f2201cc961deb3c", "title": "Eau potable (Tunisie 2009) - Drinking water", "description": "Cette application\r\nWeb va vous permettre de visualiser la desserte en eau potable en 2009 dans\r\nl’ensemble des 24 gouvernorats de la Tunisie. En cliquant sur les différentes entités\r\nvous découvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/fd975f1436e94dfc8f2201cc961deb3c" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/OnePane/main/index.html?appid=fd975f1436e94dfc8f2201cc961deb3c" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f120e5e649cc4bd6b2c1884e3b9b2595", "landingPage": "https://uneca.africageoportal.com/apps/f120e5e649cc4bd6b2c1884e3b9b2595", "title": "Evolution de la Population du Grand Tunis par délégations (1994-2004) - Greater Tunis", "description": "Cette application\r\nWeb va vous permettre de visualiser la Population dans l’ensemble des 4\r\ngouvernorats du Grand Tunis (Tunis, Ariana, Manouba et Ben Arous). En cliquant\r\nsur les différentes entités vous découvrirez les informations liées.
\r\n\r\nSource : INS Tunisia (2004).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/f120e5e649cc4bd6b2c1884e3b9b2595" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/OnePane/main/index.html?appid=f120e5e649cc4bd6b2c1884e3b9b2595" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=68566528b0aa457c8e11a8823466972b", "landingPage": "https://uneca.africageoportal.com/apps/68566528b0aa457c8e11a8823466972b", "title": "Population (Tunisie 2011)", "description": "Cette application\r\nWeb va vous permettre de visualiser la Population dans l’ensemble des 24\r\ngouvernorats en Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.Source : INS Tunisia (2011).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/68566528b0aa457c8e11a8823466972b" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://www.arcgis.com/apps/OnePane/storytelling_basic/index.html?appid=68566528b0aa457c8e11a8823466972b&webmap=a28eeabfead64ada8e793c46389c481b" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=bef12f1c7eb34441911191456887f28e", "landingPage": "https://uneca.africageoportal.com/maps/bef12f1c7eb34441911191456887f28e", "title": "Offres d'emplois (Tunisie 2007) - Jobs", "description": "Cette carte va vous permettre de visualiser les offres d’emplois dans l’ensemble des 24 gouvernorats en Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2007).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/bef12f1c7eb34441911191456887f28e" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=0cda2bf6817c4300b025ca052777a177", "landingPage": "https://uneca.africageoportal.com/apps/0cda2bf6817c4300b025ca052777a177", "title": "Offres/Demandes d'emplois en Tunisie (2007) – Jobs & Job Applications", "description": "Cette application Web va vous permettre de visualiser les offres et demandes d’emplois dans l’ensemble des 24 gouvernorats en Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/d28f5fb4ee8d4d1fa6624f9696d87ee4" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=cdd258df7614462c984e920af5b322a4", "landingPage": "https://uneca.africageoportal.com/maps/cdd258df7614462c984e920af5b322a4", "title": "Population par délégation (Ariana 2009)", "description": "Cette carte va vous permettre de visualiser la population dans l’ensemble des délégations du gouvernorat de l’Ariana en 2009. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/cdd258df7614462c984e920af5b322a4" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=b4d09962ecb542ccaa92755e3b65eb05", "landingPage": "https://uneca.africageoportal.com/maps/b4d09962ecb542ccaa92755e3b65eb05", "title": "Tozeur Equipements: Téléphones portables", "description": "Cette carte va vous permettre de visualiser les équipements en téléphones portables dans le gouvernorat de Tozeur. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia.
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/b4d09962ecb542ccaa92755e3b65eb05" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f51fafeadfbc491d89635bb9781c95fe", "landingPage": "https://uneca.africageoportal.com/maps/f51fafeadfbc491d89635bb9781c95fe", "title": "Tozeur Equipements: Ordinateurs", "description": "Cette carte va vous permettre de visualiser les équipements en Ordinateurs dans le gouvernorat de Tozeur. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia.
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/f51fafeadfbc491d89635bb9781c95fe" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=7ea599331d6c45009c560d321a0a46f2", "landingPage": "https://uneca.africageoportal.com/maps/7ea599331d6c45009c560d321a0a46f2", "title": "Tozeur Equipements: Electricité", "description": "Cette carte va vous permettre de visualiser les équipements en électricité dans le gouvernorat de Tozeur. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia.
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/7ea599331d6c45009c560d321a0a46f2" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=997d882cb6c14e11ade17e173d853b68", "landingPage": "https://uneca.africageoportal.com/maps/997d882cb6c14e11ade17e173d853b68", "title": "Tozeur Equipements: Eau potable", "description": "Cette carte va vous permettre de visualiser les équipements en Eau potable dans le gouvernorat de Tozeur. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia.
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/997d882cb6c14e11ade17e173d853b68" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=bf439395a4ed41169d3ce18aa0a4bd15", "landingPage": "https://uneca.africageoportal.com/maps/bf439395a4ed41169d3ce18aa0a4bd15", "title": "Eau potable (Tunisie 2009) - Drinking water", "description": "Cette carte va vous permettre de visualiser la desserte en eau potable en 2009 dans l’ensemble des 24 gouvernorats de la Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2009).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/bf439395a4ed41169d3ce18aa0a4bd15" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d79fec09764d49aca0a071dee5799137", "landingPage": "https://uneca.africageoportal.com/maps/d79fec09764d49aca0a071dee5799137", "title": "Evolution de la Population du Grand Tunis par délégations (1994-2004) - Greater Tunis", "description": "Cette carte va vous permettre de visualiser la Population dans l’ensemble des 4 gouvernorats du Grand Tunis (Tunis, Ariana, Manouba et Ben Arous). En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2004).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/d79fec09764d49aca0a071dee5799137" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=a28eeabfead64ada8e793c46389c481b", "landingPage": "https://uneca.africageoportal.com/maps/a28eeabfead64ada8e793c46389c481b", "title": "Population (Tunisie 2011)", "description": "Cette carte va vous permettre de visualiser la Population dans l’ensemble des 24 gouvernorats en Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2011).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/a28eeabfead64ada8e793c46389c481b" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=b629feb8819748bbac44ce0b77aa77cf", "landingPage": "https://uneca.africageoportal.com/maps/b629feb8819748bbac44ce0b77aa77cf", "title": "Demandes d'emplois (Tunisie 2007) – Job applications", "description": "Cette carte va vous permettre de visualiser les demandes d’emplois dans l’ensemble des 24 gouvernorats en Tunisie. En cliquant sur les différentes entités vous découvrirez les informations liées.
Source : INS Tunisia (2007).
Carte/Map : Graphtech (Esri's distributor in Tunisia : www.graphtech-gis.com).", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/b629feb8819748bbac44ce0b77aa77cf" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=fcca7d135a124de6b8841bdc39c01f51", "landingPage": "https://uneca.africageoportal.com/maps/esri2::standardized-precipitation-evapotranspiration-index-spei-recent-conditions", "title": "Standardized Precipitation-Evapotranspiration Index (SPEI) Recent Conditions", "description": " This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.Spatial Resolution
This image service is licensed under the Esri Master License Agreement.
This layer provides access to a 100m cell size raster derived from SRTM and other data that divides the African landscape into upland and lowland areas. The data covers Africa, Madagascar, the Comoros Islands, and other coastal Islands near Africa. It was published in 2009 by the USGS Rocky Mountain Geographic Science Center.
This layer was used as an input for the Africa Terrestrial Ecosystems mapping project.
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Created as part of the USGS’s Africa Ecosystems Mapping project, the Africa Surficial Lithology layer maps the geology of Africa into 20 classes based bedrock type and the distribution of unconsolidated surface material. The data available through this layer map the key geological features that affect the distribution of plants and ecosystems in Africa.
Dataset Summary
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
What can you do with this layer?
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n\n\n\n\n\n\n\n
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Plants play a central role in the carbon cycle by absorbing carbon dioxide from the atmosphere and incorporating it in the structure of the plant. Globally living plants contain 500 billion metric tons of carbon, more than 60 times the amount of carbon released to the atmosphere by humans each year. Understanding the distribution of the carbon stored in living plants, known as biomass, is key to estimating the effects of land use change on the climate.
Dataset Summary
This layer provides access to a 1 km cell sized raster with data on the density of carbon stored in living plants in metric tons per hectare for the year 2000. The data covers Africa, Madagascar, and other islands near Africa. It was published by the Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center in 2008.
For more information, see the Landscape Layers group on ArcGIS Online.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Location Platform account.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/esri::africa-carbon-biomass" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://landscape5.arcgis.com/arcgis/rest/services/Africa_Carbon_Biomass/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f64fbb5ac501469a8ae13543af55632b", "landingPage": "https://uneca.africageoportal.com/datasets/esri::africa-crop-sorghum-harvested-area", "title": "Africa Crop Sorghum - Harvested Area", "description": "This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. This map shows the 250 meter corrected reflectance product from both satellites that carry a MODIS, Aqua and Terra. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
A\ncentral focus for governing bodies in Africa is the need to secure the\nnecessary food sources to support their populations. It has been estimated that\nthe current production of crops will need to double by 2050 to meet future\nneeds for food production. Higher level crop-based products that can assist\nwith managing food insecurity, such as cropping watering intensities, crop\ntypes, or crop productivity, require as a starting point precise and accurate\ncropland extent maps indicating where cropland occurs. Current cropland extent\nmaps are either inaccurate, have coarse spatial resolutions, or are not updated\nregularly. An accurate, high-resolution, and regularly updated cropland area\nmap for the African continent is therefore recognised as a gap in the current\ncrop monitoring services.
Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35 South
Temporal Coverage: 2019
Spatial Resolution: 10 x 10 meter
Update Frequency: TBD
Number of Bands: 3 Bands
Parent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMAD
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Cropland is\ndefined as:
\n\n"a\npiece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is\nsowed/planted and harvestable at least once within the 12 months after the\nsowing/planting date."
This\ndefinition will exclude non-planted grazing lands and perennial crops which can\nbe difficult for satellite imagery to differentiate from natural vegetation.
\n\nThe\nprovisional cropland extent maps have a resolution of 10 metres and were built\nusing Copernicus Sentinel-2 satellite images from 2019. The cropland extent\nmaps were built separately using extensive training data from Eastern, Western,\nand Northern Africa, coupled with a Random Forest machine learning model. A\ndetailed exploration of the methods used to produce the cropland extent map can\nbe found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub\nrepository.
\n\nIndependent\nvalidation datasets suggest the following accuracies:
\n\nThe algorithms\nfor all regions tend to report more omission errors (labelling actual crops as\nnon-crops) than commission errors (labelling non-crops as crops). Where\ncommission errors occur, they tend to be focussed around wetlands and seasonal\ngrasslands which spectrally resemble some kinds of cropping.
Available Bands
Band ID | Description | Value range | Data type | NoData/Fill value |
|---|---|---|---|---|
mask | crop extent (pixel) |
|
|
|
prob | crop probability (pixel) |
|
|
|
filtered | crop extent (object-based) |
|
|
|
mask: This band\ndisplays cropped regions as a binary map. Values of 1 indicate the\npresence of crops, while a value of 0 indicates the absence of\ncropping. This band is a pixel-based cropland extent map, meaning the\nmap displays the raw output of the pixel-based Random Forest classification.
\n\nprob: This band\ndisplays the prediction probabilities for the ‘crop’ class. As this service\nuses a random forest classifier, the prediction probabilities refer to the\npercentage of trees that voted for the random forest classification. For\nexample, if the model had 200 decision trees in the random forest, and 150 of\nthe trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75\n%. Thresholding this band at > 50 % will produce a map identical\nto mask.
\n\nfiltered: This band displays cropped regions as a\nbinary map. Values of 1 indicate the presence of crops, while a value\nof 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has\nbeen filtered using an image segmentation algorithm (see this paper for\ndetails on the algorithm used). During this process, segments smaller than 1 Ha\n(100 10m x 10m pixels) are merged with neighbouring segments, resulting in a\nmap where the smallest classified region is 1 Ha in size.\nThe filtered dataset is provided as a complement to\nthe mask band; small commission errors are removed by object-based\nfiltering, and the ‘salt and pepper’ effect typical of classifying pixels is\ndiminished.
\n\n
More details on this dataset can be found here.
This work is licensed under the Esri Master License Agreement. The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2018 World Ocean Atlas.
The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representing epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies.
The EMUs are an initial objective partitioning of the ocean using longterm historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanic environment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.
", "keyword": [ "open science", "emu", "ecological marine units", "ocean", "marine", "ecosystem", "ExU" ], "issued": "2016-09-13T20:29:05.000Z", "modified": "2026-02-19T22:46:27.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_oceans", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-180.0000,-71.3458,180.0000,83.7386", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/apps/esri::ecological-marine-unit-emu-explorer" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://livingatlas.arcgis.com/emu" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b", "landingPage": "https://uneca.africageoportal.com/datasets/esri::multispectral-landsat", "title": "Multispectral Landsat", "description": "This layer includes Landsat GLS, Landsat 8, and Landsat 9 imagery for use in visualization and analysis. This layer is time enabled and includes a number band combinations and indices rendered on demand. The Landsat 8 and 9 imagery includes nine multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.
Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Multispectral Bands
| \n Band \n | \n \n Description \n | \n \n Wavelength\n (µm) \n | \n \n Spatial\n Resolution (m) \n | \n
| \n 1 \n | \n \n Coastal\n aerosol \n | \n \n 0.43 - 0.45 \n | \n \n 30 \n | \n
| \n 2 \n | \n \n Blue \n | \n \n 0.45 - 0.51 \n | \n \n 30 \n | \n
| \n 3 \n | \n \n Green \n | \n \n 0.53 - 0.59 \n | \n \n 30 \n | \n
| \n 4 \n | \n \n Red \n | \n \n 0.64 - 0.67 \n | \n \n 30 \n | \n
| \n 5 \n | \n \n Near Infrared\n (NIR) \n | \n \n 0.85 - 0.88 \n | \n \n 30 \n | \n
| \n 6 \n | \n \n SWIR 1 \n | \n \n 1.57 - 1.65 \n | \n \n 30 \n | \n
| \n 7 \n | \n \n SWIR 2 \n | \n \n 2.11 - 2.29 \n | \n \n 30 \n | \n
| \n 8 \n | \n \n Cirrus (in\n OLI this is band 9) \n | \n \n 1.36 - 1.38 \n | \n \n 30 \n | \n
| \n 9 \n | \n \n QA Band\n (available with Collection 1)* \n | \n \n NA \n | \n \n 30 \n | \n
*More about the Quality Assessment Band
TIRS Bands
| \n Band \n | \n \n Description \n | \n \n Wavelength\n (µm) \n | \n \n Spatial\n Resolution (m) \n | \n
| \n 10 \n | \n \n TIRS1 \n | \n \n 10.60 - 11.19 \n | \n \n 100 * (30) | \n
| \n 11 \n | \n \n TIRS2 \n | \n \n 11.50 - 12.51 \n | \n \n 100 * (30) | \n
*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.
Additional Usage Notes
Data Source
Landsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted in Amazon Web Services as part of their Public Data Sets program.
For information, see Landsat 8 and Landsat 9.
", "keyword": [ "Landsat on AWS", "Landsat", "Multispectral", "Multitemporal", "imagery", "landsat 8", "temporal", "MS", "landsat 9" ], "issued": "2015-03-19T16:21:14.000Z", "modified": "2026-02-06T21:52:35.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-180.0000,-65.3235,180.0000,65.3126", "license": "
This work is licensed under the Esri Master License Agreement.This layer includes Landsat 8 and 9 imagery for use in visualization and analysis. This layer is time enabled and includes the panchromatic band from the Operational Land Imager (OLI). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.
Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Additional Usage Notes
Data Source
Landsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted in Amazon Web Services as part of their Public Data Sets program.
For information, see Landsat 8 and Landsat 9.
This work is licensed under the Esri Master License Agreement.This layer includes Landsat 8 and 9 imagery for use in visualization and analysis. This layer is time enabled and includes a number of pansharpened renderings on demand. The layer includes 15m imagery rendered on-the-fly as Natural Color with DRA. It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.
Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Additional Usage Notes
Data Source
Landsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted in Amazon Web Services as part of their Public Data Sets program.
For information, see Landsat 8 and Landsat 9.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Tunisia in 2023, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Sudan in 2023, in a multiscale map (Country and State). Nationally, the average household size is 5.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Lesotho in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Côte d'Ivoire in 2023, in a multiscale map (Country, District, Region, and Department). Nationally, the average household size is 5.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Uganda in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 4.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Tunisia in 2023, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Sudan in 2023, in a multiscale map (Country and State). Nationally, the average household size is 5.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Morocco in 2023, in a multiscale map (Country, Region, and Province). Nationally, the average household size is 4.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Lesotho in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Côte d'Ivoire in 2023, in a multiscale map (Country, District, Region, and Department). Nationally, the average household size is 5.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement.
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This work is licensed under the Esri Master License Agreement.Sea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps.
The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature.
Learn more about the source data.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.
The GEOGloWS ECMWF Streamflow System represents a daily 51-member ensemble streamflow forecast for over 1 million reaches across the globe. Gridded surface runoff, provided by the European Centre for Medium-range Weather Forecasting (ECMWF), is downscaled and routed to the streams using the Routing Application for Parallel computation of Discharge (RAPID). This animation layer shows the first 6-days of a 15-day forecast at 3-hr intervals. Additionally, a 40-yr historical simulation was produced based on ECMWF’s ERA5 dataset. From this historical simulation , return periods for each reach are calculated and used to color the stream segments when/where events exceed these thresholds.
Credits: This forecast model was produced as part of the GEOGloWS Partnership with collaboration from BYU, ECMWF, esri, NOAA, NASA, SERVIR, USAID, ICIMOD, JRC, Copernicus, World Bank, and Microsoft Azure.
This work is licensed under the Esri Master License Agreement.
Sentinel-2 Level-1C imagery with on-the-fly renderings for visualization. This imagery layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.
Sentinel-2 imagery can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity.
Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Multispectral Bands
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
Additional Notes
Data Source
Sentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS, or alternatively access EarthExplorer or the Copernicus Data Space Ecosystem to download the scenes.
For information on Sentinel-2 imagery, see Sentinel-2.
", "keyword": [ "sentinel", "imagery", "multispectral", "temporal", "amazon", "MS", "esa", "sentinel on aws", "sentinel2", "sentinel2 on aws", "sentinel2 views", "sentinel-2" ], "issued": "2018-05-02T15:47:29.000Z", "modified": "2025-09-02T18:15:06.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.9973,-66.8050,179.9973,72.9957", "license": " This work is licensed under the Esri Master License Agreement.
Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.
This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available.
You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.
OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.
Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.
", "keyword": [ "baselayer3D", "OpenStreetMap", "OSM", "3D Buildings", "osm3dbuildings", "I3S" ], "issued": "2022-06-03T19:33:22.000Z", "modified": "2025-08-29T09:12:25.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": " This work is licensed under the Esri Master License Agreement.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.
Quick access to the following band combinations and indices is provided:
This work is licensed under the Esri Master License Agreement.
This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen months
Quick access to the following band combinations and indices is provided:
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
", "keyword": [ "imagery", "Sentinel", "Sentinel-2", "multispectral", "temporal", "multitemporal", "earth" ], "issued": "2018-05-22T22:05:42.000Z", "modified": "2025-08-29T00:50:18.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "
http://www.aoml.noaa.gov/phod/dac/dac_meanvel.php
Satellite-tracked SVP drifting buoys (Sybrandy and Niiler, 1991; Niiler, 2001) provide observations of near-surface circulation at unprecedented resolution. In September 2005, the Global Drifter Array became the first fully realized component of the Global Ocean Observing System when it reached an array size of 1250 drifters. A drifter is composed of a surface float which includes a transmitter to relay data, a thermometer that reads temperature a few centimeters below the air/sea interface, and a submergence sensor used to detect when/if the drogue is lost. The surface float is tethered to a holey sock drogue, centered at 15 m depth. The drifter follows the flow integrated over the drogue depth, although some slip with respect to this motion is associated with direct wind forcing (Niiler and Paduan, 1995). This slip is greatly enhanced in drifters that have lost their drogues (Pazan and Niiler, 2000). Drifter velocities are derived from finite differences of their position fixes. These velocities, and the concurrent SST measurements, are archived at AOML's Drifting Buoy Data Assembly Center where the data are quality controlled and interpolated to 1/4-day intervals (Hansen and Herman, 1989; Hansen and Poulain, 1996).
In this study, 6h winds were interpolated onto the drifter positions and used to estimate and remove the slip (Niiler and Paduan, 1995; Pazan and Niiler, 2000; Laurindo et al., 2017). All velocities and SSTs were lowpassed at 1.5 times the local inertial period, or five days if that is smaller, to remove high frequency variability (diurnal, tidal, inertial). If 1.5 times the local inertial period is shorter than one day, the lowpass is done at one day.
Drifters sample regions of the ocean inhomogeneously, which can cause aliased time-mean values if strong seasonal or interannual variations are neglected. To address the seasonal cycle, Lumpkin (2003) developed a methodology to simultaneously decompose the drifter observations into time-mean, seasonal and eddy components using a Gauss-Markov approach that produces formal error bars on all components. Lumpkin showed that this methodology produces significantly different results than standard bin averaging. This methodology was further developed and evaluated using SST observations and products, and simulated drifters in the MICOM model (Lumpkin and Garraffo, 2005). The method produces significantly improved estimates of the mean currents and SST, and simultaneously provides the annual and semiannual amplitudes and phases at a nominal resolution of one degree squared.
To address inhomogeneous interannual sampling associated with ENSO, Johnson (2001) added a component proportional to a five-month lowpassed Southern Oscillation Index, and estimated components in elliptical bins with axes scaled and oriented using the residual variability (i.e., the eddy fluctuations).
In Lumpkin and Johnson (2013), the methodologies of Lumpkin (2003) and Johnson (2001) are combined. The observations are projected onto a time mean, annual and semiannual, SOI, and spatial gradient components within elliptical bins scaled and oriented using eddy fluctuations, and error bars are estimated for all terms.
In Laurindo et al. (2017), slip correction using a spatially-varying coefficient was introduced in order to exploit velocity information from undrogued drifters. This approximately doubles the number of available observations, which allowed the bin sizes to be considerably reduced (one degree radius circles). In addition, 1D spatial fitting was done in the direction that maximizes the explained variance, allowing for greater resolution of cross-stream velocity gradient. These two changes result in significantly better spatial resolution of ocean current structure. In addition, Laurindo et al. examined how the formal standard errors compare to actual errors when using a "toy" dataset of surface currents derived from AVISO and subsampled at the drifter observation locations and times; they found that formals errors underestimate actual errors by approximately a factor of 2.
When these more advanced methodologies are applied to the modern data set of tropical Atlantic drifter observations, many features of the near-surface circulation become apparent which were not resolved by older ship-drift-based climatologies or by SEQUAL/FOCAL drifter trajectories (Lumpkin and Garzoli, 2005). In the Hawaiian Island region, the thousand-kilometer long island wake is revealed at unprecedented detail (Lumpkin and Flament, 2013) Zonally elongated mid-ocean striations are resolved in the zonal currents of all major ocean basins (Laurindo et al., 2017).
Please cite as reference:
Laurindo, L., A. Mariano, and R. Lumpkin, 2017: An improved near-surface velocity climatology for the global ocean from drifter observations Deep-Sea Res. I, 124, pp.73-92, doi:10.1016/j.dsr.2017.04.009
GeoMAD is\nthe Digital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for longer-term\ntime series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has\ntwo main components: Geomedian and Median Absolute Deviations (MADs)
\n\nThe\ngeomedian component combines measurements collected over the specified\ntimeframe to produce one representative, multispectral measurement for every\npixel unit of the African continent. The end result is a comprehensive dataset\nthat can be used to generate true-colour images for visual inspection of\nanthropogenic or natural landmarks. The full spectral dataset can be used to\ndevelop more complex algorithms.
\n\nFor each\npixel, invalid data is discarded, and remaining observations are mathematically\nsummarised using the geomedian statistic. Flyover coverage provided by\ncollecting data over a period of time also helps scope intermittently cloudy\nareas.
\n\nVariations\nbetween the geomedian and the individual measurements are captured by the three\nMedian Absolute Deviation (MAD) layers. These are higher-order statistical\nmeasurements calculating variation relative to the geomedian. The MAD layers\ncan be used on their own or together with geomedian to gain insights about the\nland surface and understand change over time.
Key Properties
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
B02 | Geomedian B02 (Blue) |
|
|
|
B03 | Geomedian B03 (Green) |
|
|
|
B04 | Geomedian B04 (Red) |
|
|
|
B05 | Geomedian B05 (Red edge 1) |
|
|
|
B06 | Geomedian B06 (Red edge 2) |
|
|
|
B07 | Geomedian B07 (Red edge 3) |
|
|
|
B08 | Geomedian B08 (Near infrared (NIR) 1) |
|
|
|
B8A | Geomedian B8A (NIR 2) |
|
|
|
B11 | Geomedian B11 (Short-wave infrared (SWIR) 1) |
|
|
|
B12 | Geomedian B12 (SWIR 2) |
|
|
|
SMAD | Spectral Median Absolute Deviation |
|
|
|
EMAD | Euclidean Median Absolute Deviation |
|
|
|
BCMAD | Bray-Curtis Median Absolute Deviation |
|
|
|
COUNT | Number of clear observations |
|
|
|
Bands can be subdivided as follows:
\n\n
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This work is licensed under the Esri Master License Agreement.GeoMAD is the\nDigital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for\nlonger-term time series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has two\nmain components: Geomedian and Median Absolute Deviations (MADs).
\n\nThe geomedian component combines measurements\ncollected over the specified timeframe to produce one representative,\nmultispectral measurement for every pixel unit of the African continent. The\nend result is a comprehensive dataset that can be used to generate true-colour\nimages for visual inspection of anthropogenic or natural landmarks. The full\nspectral dataset can be used to develop more complex algorithms.
\n\nFor each pixel, invalid data is discarded, and\nremaining observations are mathematically summarised using the geomedian\nstatistic. Flyover coverage provided by collecting data over a period of time\nalso helps scope intermittently cloudy areas.
\n\nVariations between the geomedian and the\nindividual measurements are captured by the three Median Absolute Deviation\n(MAD) layers. These are higher-order statistical measurements calculating\nvariation relative to the geomedian. The MAD layers can be used on their own or\ntogether with geomedian to gain insights about the land surface and understand\nchange over time.
Key Properties
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
SR_B2 | Geomedian SR_B2 (Blue) |
|
|
|
SR_B3 | Geomedian SR_B3 (Green) |
|
|
|
SR_B4 | Geomedian SR_B4 (Red) |
|
|
|
SR_B5 | Geomedian SR_B5 (NIR) |
|
|
|
SR_B6 | Geomedian SR_B6 (SWIR 1) |
|
|
|
SR_B7 | Geomedian SR_B7 (SWIR 2) |
|
|
|
SMAD | Spectral Median Absolute Deviation |
|
|
|
EMAD | Euclidean Median Absolute Deviation |
|
|
|
BCMAD | Bray-Curtis Median Absolute Deviation |
|
|
|
COUNT | Number of clear observations |
|
|
|
More details on this dataset can be found here.
Water\nObservations from Space (WOfS) is a service that draws on satellite imagery to\nprovide historical surface water observations of the whole African continent.\nWOfS allows users to understand the location and movement of inland and coastal\nwater present in the African landscape. It shows where water is usually\npresent; where it is seldom observed; and where inundation of the surface has\nbeen observed by satellite.
WOfS annual summary shows the frequency of a pixel being classified as wet over an annual period (calendar year). This is calculated by looking at:
Key Properties
Available Bands
Band ID | Description | Value range | Data type | No data value |
|---|---|---|---|---|
count_wet | How many times a pixel was wet |
|
|
|
count_clear | How many times a pixel was clear |
|
|
|
frequency | Frequency of water detection at a location |
|
|
|
Interpreting WOfS
The WOfS service should be interpreted with caveats in the following situations:
Note that WOfS is not intended for studying ocean. Validation has been centred around inland and near-coastal waterbodies.
\n\nMore details on this dataset can be found here.
", "keyword": [ "wofs_ls", "wofs_ls_summary_annual", "Digital Earth Africa", "Water Observation from Space", "Landsat", "DE Africa", "Surface water", "Open water" ], "issued": "2022-01-13T03:33:00.000Z", "modified": "2025-05-02T11:02:19.523Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DigitalEarthAfrica", "hasEmail": "mailto: africageoportal@esri.com" }, "accessLevel": "public", "spatial": "-26.8639,-49.4462,65.6697,40.9521", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/africageoportal::water-observations-from-space-annual-statistics-for-africa-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://di-deafrica.img.arcgis.com/arcgis/rest/services/deafrica/WOFS_Annual_Frequency/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=e221cbaf69d140a88aeaaaf9a5541952", "landingPage": "https://uneca.africageoportal.com/maps/esri::plate-boundaries", "title": "Plate Boundaries", "description": "
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.COUNTRY OF BURUNDI
ECA supported the Institute of Statistics and Economic Studies of Burundi (ISTEEBU) [1] in Census Cartography and Geospatial data management, as well as to develop a Geocoding scheme for the geospatial analysis of 2024 census data. The three missions to Burundi's Bureau of the Census, discussed issues, including but not limited to the census cartographic strategy document, census mapping project management structures, training and recruitment of census mapping staff, quality assurance and control, sustainability and risk factors, census methodology, communication, publicity and advocacy plan, mapping operations and budgeting in the field, update of locality coding list and geocoding scheme, spatial analysis of census results, analysis plan according to the thematic topics of the census and dissemination of results.
The Country Level Impacts of Climate Change (CLICC) project is supported by the United Nations Environment Programme (UNEP) and the UK Department of Energy and Climate Change (DECC). The project will work closely with UNEP’s Global Programme of Research on Climate Change Vulnerability, Impacts and Adaptation (PROVIA). CLICC was initiated with the aim to develop a common process for countries to present the impacts of climate change at the national level, drawing on existing national assessments and other research material.
This map shows the countries involved in the Durban Project as well as those that were consulted during the first phase of the CLICC project, between July and December 2014. These represent a range of levels of economic development and geographies, including islands, deserts, mountainous, land-locked and coastal regions. Where Impact Assessments were carried out, these are also highlighted, with links to the respective documents.
", "keyword": [ "Emissions", "Impacts", "Climate Change", "CLICC", "Global", "climate change", "climate change related planning and management", "death", "climate change and its impacts", "emissions", "impacts", "clicc", "2014", "archive" ], "issued": "2016-11-08T05:29:43.000Z", "modified": "2022-12-08T09:20:41.000Z", "publisher": { "name": "UNEP, Office of Science" }, "contactPoint": { "@type": "vcard:Contact", "fn": "uneplive_team", "hasEmail": "mailto:unep-mapping-validation@un.org" }, "accessLevel": "public", "spatial": "-180.0000,-74.0439,180.0000,76.4964", "license": "Source: UNEP", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/uneplive::country-level-impacts-of-climate-change-clicc-1" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=a4a6b1f779be4b64816d1876cfe669b9", "landingPage": "https://uneca.africageoportal.com/apps/LandscapeTeam::world-terrestrial-ecosystems", "title": "World Terrestrial Ecosystems", "description": "Terrestrial ecosystems can be defined by their climate, landform and land cover. The World Terrestrial Ecosystems Map identifies areas with similar terrestrial ecosystem structure.
Ecosystems are distinct areas on the planet which differ based on their environmental settings and assemblages of organisms.
Online explorer tool produced by the U.S Geological Survey, the World Terrestrial Ecosystems Explorer (WTEE), allows for the map-based visualization and query of any terrestrial location on Earth for its ecosystem type and characteristics.
", "keyword": [ "World Ecosystem", "SDGs", "UNSDG", "SDG15", "WorldClimateRegions", "Ecology" ], "issued": "2019-11-13T23:51:38.000Z", "modified": "2022-12-05T21:23:18.000Z", "publisher": { "name": "Living Atlas – Landscape Content " }, "contactPoint": { "@type": "vcard:Contact", "fn": "Deniz_Landscape", "hasEmail": "mailto:cfrye@esri.com" }, "accessLevel": "public", "spatial": "", "license": "This work is licensed under the Esri Master License Agreement.
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This work is licensed under the Esri Master License Agreement.
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This work is licensed under the Esri Master License Agreement.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
| (1) Including Agalega, Rodrigues and Saint Brandon. |
| (2) Including Zanzibar. |
| (3) Including Ascension and Tristan da Cunha. |
| (4) For statistical purposes, the data for China do not include Hong Kong and Macao, Special Administrative Regions (SAR) of China, and Taiwan Province of China. |
| (5) As of 1 July 1997, Hong Kong became a Special Administrative Region (SAR) of China. |
| (6) As of 20 December 1999, Macao became a Special Administrative Region (SAR) of China. |
| (7) The regions Southern Asia and Central Asia are combined into South-Central Asia. |
| (8) Including Sabah and Sarawak. |
| (9) Including Nagorno-Karabakh. |
| (10) Refers to the whole country |
| (11) Including Abkhazia and South Ossetia. |
| (12) Including East Jerusalem. |
| (13) Including Transnistria. |
| (14) Including Crimea |
| (15) Refers to Guernsey, and Jersey. |
| (16) Including Åland Islands. |
| (17) Including Svalbard and Jan Mayen Islands. |
| (18) Refers to the Vatican City State. |
| (19) Including Kosovo. |
| (20) Including Canary Islands, Ceuta and Melilla. |
| (21) The former Yugoslav Republic of Macedonia. |
| (22) Refers to Bonaire, Saba and Sint Eustatius. |
| (23) Including Saint-Barthélemy and Saint-Martin (French part). |
| (24) Including Christmas Island, Cocos (Keeling) Islands and Norfolk Island. |
| (25) Including Pitcairn. |
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UrbanObservatory::country-population-estimates-2015-from-united-nations" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=81aa7c52d70a4bdf8859965062e2db3d&sublayer=0", "landingPage": "https://uneca.africageoportal.com/maps/UrbanObservatory::malaria-prevalence-among-children-in-africa-2015", "title": "Malaria Prevalence among Children in Africa, 2015", "description": "Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. This layer highlights malaria rates among children age 2 to 10 in Sub-Saharan Africa in 2015. This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UrbanObservatory::malaria-prevalence-among-children-in-africa-2015" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tiles.arcgis.com/tiles/4yjifSiIG17X0gW4/arcgis/rest/services/2015_Malaria_Prevalence_among_Children_in_Sub_Saharan_Africa/MapServer/0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=cfd5a083aea84f81941d1d8a81466636&sublayer=0", "landingPage": "https://uneca.africageoportal.com/maps/UrbanObservatory::malaria-prevalence-among-children-in-africa-2000", "title": "Malaria Prevalence among Children in Africa, 2000", "description": "Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. This layer highlights malaria rates among children age 2 to 10 in Sub-Saharan Africa in 2000. This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UrbanObservatory::malaria-prevalence-among-children-in-africa-2000" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tiles.arcgis.com/tiles/4yjifSiIG17X0gW4/arcgis/rest/services/2000_Malaria_Prevalence_among_Children_in_Sub_Saharan_Africa____/MapServer/0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f741ce2f7a604826a37562e28d737b27&sublayer=0", "landingPage": "https://uneca.africageoportal.com/maps/UrbanObservatory::change-in-malaria-prevalence-among-children-in-africa-2000-to-2015", "title": "Change in Malaria Prevalence among Children in Africa, 2000 to 2015", "description": "Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. Treatment and prevention efforts such as insecticide-treated mosquito nets and rapid diagnostic tests significantly decreased the number of malaria cases in Africa. This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UrbanObservatory::change-in-malaria-prevalence-among-children-in-africa-2000-to-2015" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tiles.arcgis.com/tiles/4yjifSiIG17X0gW4/arcgis/rest/services/Change_in_Malaria_Prevalence_among_ChildrenVersion2/MapServer/0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ae78aeb913a343d69e950b53e29076f7", "landingPage": "https://uneca.africageoportal.com/maps/UNEP-WCMC::wdpa-world-database-of-protected-areas", "title": "WDPA - World Database of Protected Areas", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nWorld Database of Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UNEP-WCMC::wdpa-world-database-of-protected-areas" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v0/FeatureServer" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ae78aeb913a343d69e950b53e29076f7&sublayer=0", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-point-latest", "title": "WDPA point Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nWorld Database of Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-point-latest" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v0/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/sqlite?layers=0" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ae78aeb913a343d69e950b53e29076f7&sublayer=1", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-poly-latest", "title": "WDPA poly Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nWorld Database of Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-poly-latest" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v0/FeatureServer/1" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/csv?layers=1" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/shapefile?layers=1" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geojson?layers=1" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/kml?layers=1" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/filegdb?layers=1" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/featureCollection?layers=1" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/excel?layers=1" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geoPackage?layers=1" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/sqlite?layers=1" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ae78aeb913a343d69e950b53e29076f7&sublayer=2", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-source-latest-4", "title": "WDPA source Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nWorld Database of Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-source-latest-4" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v0/FeatureServer/2" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/csv?layers=2" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/shapefile?layers=2" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geojson?layers=2" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/kml?layers=2" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/filegdb?layers=2" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/featureCollection?layers=2" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/excel?layers=2" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/geoPackage?layers=2" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/ae78aeb913a343d69e950b53e29076f7/sqlite?layers=2" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=8fd234a5b08a4859885abda763f35b93", "landingPage": "https://uneca.africageoportal.com/maps/UNEP-WCMC::wdpa-marine-and-coastal-protected-areas", "title": "WDPA - Marine and Coastal Protected Areas", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nMarine and Coastal Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/maps/UNEP-WCMC::wdpa-marine-and-coastal-protected-areas" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v4/FeatureServer" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=8fd234a5b08a4859885abda763f35b93&sublayer=0", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-point-latest-4", "title": "WDPA point Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nMarine and Coastal Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-point-latest-4" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v4/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/kml?layers=0" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/filegdb?layers=0" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/featureCollection?layers=0" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/excel?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geoPackage?layers=0" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/sqlite?layers=0" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=8fd234a5b08a4859885abda763f35b93&sublayer=1", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-poly-latest-4", "title": "WDPA poly Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nMarine and Coastal Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-poly-latest-4" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v4/FeatureServer/1" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/csv?layers=1" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/shapefile?layers=1" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geojson?layers=1" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/kml?layers=1" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/filegdb?layers=1" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/featureCollection?layers=1" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/excel?layers=1" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geoPackage?layers=1" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/sqlite?layers=1" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=8fd234a5b08a4859885abda763f35b93&sublayer=2", "landingPage": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-source-latest-1", "title": "WDPA source Latest", "description": "The World Database on Protected and Conserved Areas (WDPCA) is a joint product of UNEP and IUCN, managed by UNEP-WCMC, working with governments and other data providers. The database stores information on the global distribution of terrestrial, inland water and marine protected areas and other effective area-based conservation measures (OECMs). The WDPCA combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM).
\n\nThe database contains data on protected areas and OECMs designated at the national level and under regional and international conventions and agreements, in addition to those recognised through other means. The WDPCA adopts the definitions of a protected area used by IUCN and the Convention on Biological Diversity (CBD), and the definition of an OECM adopted by Parties to the CBD.
\n\nA User Manual (UNEP-WCMC 2026) is provided with the dataset. This includes a description of the attribute table and guide to interpreting its fields. The WDPCA can be viewed at, and downloaded from, Protected Planet (https://www.protectedplanet.net/) or from IBAT for commercial purposes (https://www.ibat-alliance.org/).
\n\nA new version is published each month. The latest version is available from https://www.protectedplanet.net/.
\n\nMarine and Coastal Protected Areas query:
\n\nAll of these expressions must be true:
\n\nNo commercial use: Neither (a) the WDPCA Materials and the GD-PAME Materials nor (b) any work derived from or based upon the WDPCA Materials and the GD-PAME Materials (“Derivative Works”) may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use” means a) any use by, on behalf of, or to inform or assist the activities of, a commercial entity (an entity that operates 'for profit') or b) use by any individual or non-profit entity for the purposes of revenue generation. For commercial uses, please go to the IBAT website: https://www.ibat-alliance.org/. For the avoidance of doubt, UNEP-WCMC reserves the right to determine whether a particular use of the WDPCA and GD-PAME Materials constitutes a Commercial Use or otherwise.
No Sub-licensing or Redistribution of WDPCA and GD-PAME Data: The WDPCA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPCA or Data contained in the WDPCA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPCA Data please refer them to ProtectedPlanet.net. If you wish to provide a service through which the WDPCA Data are downloadable or otherwise made available for redistribution you must contact pca@unep-wcmc.org for permission and further guidance.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/UNEP-WCMC::wdpa-source-latest-1" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services5.arcgis.com/Mj0hjvkNtV7NRhA7/arcgis/rest/services/WDPA_v4/FeatureServer/2" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/csv?layers=2" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/shapefile?layers=2" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geojson?layers=2" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/kml?layers=2" }, { "@type": "dcat:Distribution", "title": "File Geodatabase", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/filegdb?layers=2" }, { "@type": "dcat:Distribution", "title": "Feature Collection", "format": "TXT", "mediaType": "text/plain", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/featureCollection?layers=2" }, { "@type": "dcat:Distribution", "title": "Excel", "format": "XLSX", "mediaType": "application/vnd.ms-excel", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/excel?layers=2" }, { "@type": "dcat:Distribution", "title": "GeoPackage", "format": "GPKG", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/geoPackage?layers=2" }, { "@type": "dcat:Distribution", "title": "SQLite Geodatabase", "format": "GDB", "mediaType": "application/geopackage+sqlite3", "accessURL": "https://uneca.africageoportal.com/api/download/v1/items/8fd234a5b08a4859885abda763f35b93/sqlite?layers=2" }, { "@type": "dcat:Distribution", "title": "Protected Planet", "format": null, "accessURL": "https://www.protectedplanet.net/", "description": null }, { "@type": "dcat:Distribution", "title": "WDPCA web feature service", "format": null, "accessURL": "https://data-gis.unep-wcmc.org/server/rest/services/ProtectedPlanet/WDPCA/FeatureServer", "description": null } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=831615388b7f42799542489f12b94602", "landingPage": "https://uneca.africageoportal.com/apps/esri::water-balance-app", "title": "Water Balance App", "description": "Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.
Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.
This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.
", "keyword": [ "GLDAS", "NASA", "hydrology", "earth observations", "landscape", "climate", "weather" ], "issued": "2017-09-28T18:12:28.000Z", "modified": "2026-10-27T17:38:19.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_hydro", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.0000,-89.9900,179.0000,89.9900", "license": " This work is licensed under the Esri Master License Agreement.
Elephants are the largest terrestrial living species. They are herbivorous animals and require 100 kilograms to 200 kilograms of food and about 230 liters of water each day. Their home range can expand up to 11,000 square kilometers. Their ability to find food and water sources is gained from traditional knowledge learned over generations. This knowledge, which is important for survival, is lost if elder elephants of the herd perish.
Elephants are endangered due to many reasons. They can be killed by poachers for their tusks, or captured and tamed for social status and for the circus. Changes in environment such as global warming, rain patterns, deforestation, and mining can lead to degradation of their habitat, forcing these animals to move to different areas in search of food and water. This can cause conflicts with humans as elephants move into human settlements and farmlands. They can also run into electrical fences and traps.
To avoid life-threatening incidents, and for their conservation, monitoring the elephants and their movements is of high importance. It is easier to monitor the elephants using aerial imagery, as it does not require human intervention or disturbance in elephants' natural habitat. Elephant Detection using aerial imagery is more efficient when performed over vast areas. This deep learning model helps automate the task of detecting elephants from high-resolution aerial imagery.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
High resolution RGB imagery (3-13 centimeters spatial resolution).
Output
Feature class containing detected elephants.
Applicable geographies
The model is expected to work well with aerial imagery of southern African forests (South Africa, Botswana, and Namibia) or similar geographies.
Model architecture
This model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 0.857 for elephant.
Training data
The model has been trained on the The Aerial Elephant Dataset.
Limitations
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::elephant-detection" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=de857cf9ae344f58b8f8114a9b108161", "landingPage": "https://uneca.africageoportal.com/apps/westerncapegov::western-cape-department-of-cultural-affairs-and-sports-dcas-facilities-app-", "title": "Western Cape Department of Cultural Affairs and Sport's (DCAS) Facilities App ", "description": "The purpose of this web app is to provide the locations of the cultural and sport facilities under the auspices of the Western Cape government's Department of Cultural Affairs and Sport (DCAS).Follow the guide to use the model. This model requires deep learning libraries to be installed, install them using Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
8-bit, 3-band high-resolution (60 cm) imagery.
Output
Feature class containing detected wind turbines.
Applicable geographies
The model is expected to work well across USA and Netherlands.
Model architecture
This model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 0.96.
Training data
This model has been trained on an Esri proprietary wind turbines dataset.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement.
This deep learning model is based on DeepForest and has been trained on data from the National Ecological Observatory Network (NEON). The model also uses Segment Anything Model (SAM) by Meta.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model cannot be fine-tuned using ArcGIS tools.
Input
8 bit, 3-band high-resolution (10-25 cm) imagery.
Output
Feature class containing separate masks for each tree.
Applicable geographies
The model is expected to work well in the United States.
Model architecture
This model is based upon the DeepForest python package which uses the RetinaNet model architecture implemented in torchvision and open-source Segment Anything Model (SAM) by Meta.
Accuracy metrics
This model has an precision score of 0.66 and recall of 0.79.
Training data
This model has been trained on NEON Tree Benchmark dataset, provided by the Weecology Lab at the University of Florida. The model also uses Segment Anything Model (SAM) by Meta that is trained on 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.
Sample results
Here are a few results from the model.
Citations
Weinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309
Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071
", "keyword": [ "Segment anything", "dlpk", "LivingAtlasDLPK", "tree", "detection", "Segmentation" ], "issued": "2023-05-18T13:22:25.000Z", "modified": "2026-09-01T08:34:59.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_analytics", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-131.4840,20.7970,-58.7110,53.8530", "license": " This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::tree-segmentation" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=55600a3a452c4b208d3c54026c3f7cd1", "landingPage": "https://uneca.africageoportal.com/content/esri::solar-photovoltaic-park-classification-global", "title": "Solar Photovoltaic Park Classification - Global", "description": "Solar power is a clean source of energy. To replace conventional power sources, solar power generation must be scaled. This is being done by creating large solar photovoltaic parks. Some of large parks even span up to thousands of acres and are equipped with millions of solar panels. This information can serve government, policy makers, international organizations, companies, researchers in various ways.
Traditional ways of obtaining information on solar photo voltaic parks, such as surveys and on-site visits, are time consuming and error prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of creating classified maps indicating concentration of solar parks, reducing time and effort required significantly.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
Sentinel-2 L2A imagery (bottom of atmosphere) having 12 bands and 10 meter resolution in form of a raster product, mosaic dataset, or image service.
Output
Classified raster denoting solar photovoltaic parks.
Applicable geographies
The model is expected to work well globaly.
Model architecture
This model uses the UNet model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an overall accuracy of 99.0 %. Solar photovoltaic park class has a precision of 0.966, recall of 0.967 and F1 score of 0.967.
Training data
This model has been trained on an Esri proprietary solar photovoltaic park classification dataset.
Limitations
• False positives are observed near costal areas, mountains and cloudy regions.
Sample results
Here are a few results from the model. To view more, see this web map.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::solar-photovoltaic-park-classification-global" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=28755e99bbde42508f22b957681a70e2", "landingPage": "https://uneca.africageoportal.com/content/esri::shipwreck-detection", "title": "Shipwreck Detection", "description": "Shipwrecks are a potential threat to the ships passing by on the surface. Marking them manually is a complex and time-consuming task. Deep learning can be used to significantly optimize and automate this task. This model can be used as-is or fine-tuned to adapt to your own data/geography.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Input
BAG data (preferrably at a cell size of 0.5m).
Output
Feature class with detected shipwrecks as polygons.
Applicable geographies
The model is expected to work for any marine geography.
Model architecture
This model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
The model has an average precision score of 0.921 percent on our validation dataset.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::shipwreck-detection" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=705f4c04ac3043be806529047b79abfd", "landingPage": "https://uneca.africageoportal.com/content/esri::ship-detection-sar", "title": "Ship Detection (SAR)", "description": "Ship detection plays an important role in port management, in terms of ship traffic, maritime rescue, cargo transportation and national defense. Satellite imagery provides data with high spatial and temporal resolution, which is useful for ship detection. SAR data has advantages over optical data, as microwaves are capable of penetrating clouds and can be used in all types of weather. SAR data is also useful for locating ships during storms for rescue missions.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::ship-detection-sar" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=9b67b441f29f4ce6810979f5f0667ebe", "landingPage": "https://uneca.africageoportal.com/content/esri::segment-anything-model-sam", "title": "Segment Anything Model (SAM)", "description": "Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.
Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training.
SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.
Using the model
8-bit, 3-band imagery.
Output
Feature class containing masks of various objects in the image.
Applicable geographies
The model is expected to work globally.
Model architecture
This model is based on the open-source Segment Anything Model (SAM) by Meta.
Training data
This model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::segment-anything-model-sam" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4019a53c914947aea9621ba226ec8861", "landingPage": "https://uneca.africageoportal.com/content/esri::seabird-tern-detection-africa", "title": "Seabird (Tern) Detection - Africa", "description": "Seabirds are found near marine habitats, such as sea and wetlands, due to food availability. They mostly feed on fish and insects. The seabird population is declining at a much faster rate compared to other birds as the coastal region is sensitive to pollution, commercial fisheries, habitat degradation, mineral extraction, human disturbance, etc. Seabirds are also endangered by predatory species from both land and water. Apart from the geography of their habitat, they do not have much ability to defend their nest or protect their young ones. Breeding and laying of eggs happen in open habitats, such as bare ground and open sandy or rocky areas, on coasts and islands with little or no nest material.
The Royal tern and Caspian tern are two of the 350 odd seabird species. These adult terns could be of size 45-60 cm weighing 350-750 gm. Their size puts them in the category of small objects and thus we need very high-resolution imagery to detect them. Recent innovations in drones and AI have enabled us to capture high-resolution imagery over a large geographic area and detect objects of different shapes and sizes. Drones also decrease the disturbance to bird population. Drones are easier to deploy and can perform frequent surveys even after disasters like hurricanes, oil spills, etc. This deep learning model helps automate the task of detecting seabirds (Royal and Caspian terns) from high-resolution aerial imagery. This can help in mapping effective site protection areas for seabirds.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
High resolution RGB imagery (1.0 cm resolution).
Output
Feature class containing detected seabirds.
Applicable geographies
The model is expected to work well with aerial imagery of West African coast or similar geographies.
Model architecture
This model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 0.76 for seabird.
Training data
The model has been trained on the Aerial Seabirds West Africa.
Limitations
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::seabird-tern-detection-africa" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=6ce6dae2d62c4037afc3a3abd19afb11", "landingPage": "https://uneca.africageoportal.com/content/esri::power-line-classification", "title": "Power Line Classification", "description": "The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive.
The model accepts unclassified point clouds with point geometry (X, Y and Z values).
Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly.
| Classcode | Class Description |
| 0 | Background Class |
| 14 | Distribution Wires |
| 15 | Distribution Tower/Poles |
Applicable geographies
The model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.
Model architecture
This model uses the RandLANet model architecture implemented in ArcGIS API for Python.
Accuracy metrics
The table below summarizes the accuracy of the predictions on the validation dataset.
| - | Precision | Recall | F1-score |
| Background (0) | 0.999679 | 0.999876 | 0.999778 |
| Distribution Wires (14) | 0.955085 | 0.936825 | 0.945867 |
| Distribution Poles (15) | 0.707983 | 0.553888 | 0.621527 |
| X, Y, and Z linear unit | meter |
| Z range | -240.34 m to 731.17 m |
| Number of Returns | 1 to 5 |
| Intensity | 1 to 4095 |
| Point spacing | 0.2 ± 0.1 |
| Scan angle | -42 to +35 |
| Maximum points per block | 20000 |
| Extra attributes | None |
| Class structure | [0, 14, 15] |
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::power-line-classification" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=3073e0d82ec04db497c132352bd84a33", "landingPage": "https://uneca.africageoportal.com/content/esri::plant-leaf-disease-classification", "title": "Plant Leaf Disease Classification", "description": "Classified image of the leaf with any of the plant disease, healthy leaf, or background classes as in the Plant Leaf Diseases dataset.
Applicable geographies
This model is expected to work well in all regions globally. However, results can vary for images that are statistically dissimilar to training data.
Model architecture
This model uses the ResNet50 model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an overall accuracy of 97.88 percent. The confusion matrix below summarizes the performance of the model on the validation dataset.
Here are a few results from the model:
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::plant-leaf-disease-classification" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=916e02960d9e495baeb4d1d2ff4055d0", "landingPage": "https://uneca.africageoportal.com/content/esri::palm-tree-detection", "title": "Palm Tree Detection", "description": "Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
High resolution RGB imagery (5 - 15 centimeter spatial resolution).
Output
Feature class containing detected palm trees.
Applicable geographies
The model is expected to work well globally.
Model architecture
This model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 75 percent.
Training data
This model has been trained on an Esri proprietary palm tree detection dataset.
Sample results
Here are a few results from the model. To view more, see this story.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::palm-tree-detection" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4dd65af881f64236ac9bbaa407e046ba", "landingPage": "https://uneca.africageoportal.com/content/esri::oil-spill-detection-sar", "title": "Oil Spill Detection (SAR)", "description": "Oil spills are a major source of marine pollution that affect the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink down to the seabed affecting sedimentation rates. While many oil spills are accidental, some are caused deliberately by cargo ships dumping waste oil and bilge water. It is very difficult to identify, detect and remove oil from the ocean surface and routine monitoring can help prevent illegal dumping and aid with remediation efforts.
This deep learning model automates the task of detecting potential oil spills from Sentinel-1 SAR data. In addition to being inexpensive, SAR data is collected day and night in all weather conditions without getting affected by cloud cover. Use this model to identify potential oil spills that need to be reviewed or monitored, reducing time and effort required significantly.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
32-bit floating point, 3-band Sentinel-1 C band SAR GRD VV polarization band raster.
Output
Feature layer representing oil slick.
Applicable geographies
The model is expected to work globally.
Model architecture
The model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
The model has an average precision score of 0.69.
Training data
This model is trained on 381 Sentinel-1 scenes downloaded from the ASF portal, and the ground truth data from NESDIS Marine Pollution Products.
Sample results
Here are a few results form the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::oil-spill-detection-sar" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=741a56ae6a5340058b9704a8f68f1b9a", "landingPage": "https://uneca.africageoportal.com/content/esri::mangrove-classification-landsat-8", "title": "Mangrove Classification (Landsat 8)", "description": "Mangroves are critical to the maintenance and conservation of healthy coastal ecosystems. They are extremely rich in biodiversity and are home to a diverse set of plant and animal species. Mangroves protect inland coastal areas from erosion and storm surge impacts such as from tsunamis. These factors make mangrove monitoring and conservation an important activity. Due to various climatic phenomena and coastal activity such as reclamation for development, mangrove forests are rapidly shrinking. This warrants active monitoring and conservation efforts of mangroves. This deep learning model enables rapid monitoring of mangrove forests.
Using the model
Follow this guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
Surface Reflectance (Collection 2 Level 2) imagery acquired by the Landsat 8 Sensor in form of a raster, mosaic dataset or image service.
Output
Classified raster containing two classes: mangrove and other.
Applicable geographies
This model is expected to work well globally.
Model architecture
This model uses the UNet model architecture implemented in ArcGIS API for Python.
Accuracy metrics
The mangrove class has a precision of 0.85, recall of 0.80 and F1 score of 0.82.
Training data
This model has been trained on the Global Mangrove Watch dataset with Landsat 8 Scenes from the same time.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::mangrove-classification-landsat-8" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=afd124844ba84da69c2c533d4af10a58", "landingPage": "https://uneca.africageoportal.com/content/esri::land-cover-classification-sentinel-2", "title": "Land Cover Classification (Sentinel-2)", "description": "Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics, giving superior results.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
Classified raster with the same classes as in Corine Land Cover (CLC) 2018.
Applicable geographies
This model is expected to work well in Europe and the United States.
This model has an overall accuracy of 82.41% with Level-1C imagery and 84.0% with Level-2A imagery, for CLC class level 2 classification (15 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.
| Class | Level-2A Imagery | Level-1C Imagery | ||||
| Precision | Recall | F1 Score | Precision | Recall | F1 Score | |
| Urban fabric | 0.81 | 0.83 | 0.82 | 0.82 | 0.84 | 0.83 |
| Industrial, commercial and transport units | 0.74 | 0.65 | 0.69 | 0.73 | 0.66 | 0.7 |
| Mine, dump and construction sites | 0.63 | 0.52 | 0.57 | 0.69 | 0.55 | 0.61 |
| Artificial, non-agricultural vegetated areas | 0.70 | 0.46 | 0.55 | 0.67 | 0.47 | 0.55 |
| Arable land | 0.86 | 0.90 | 0.88 | 0.86 | 0.89 | 0.87 |
| Permanent crops | 0.76 | 0.73 | 0.74 | 0.75 | 0.71 | 0.73 |
| Pastures | 0.75 | 0.71 | 0.73 | 0.74 | 0.71 | 0.73 |
| Heterogeneous agricultural areas | 0.61 | 0.56 | 0.58 | 0.62 | 0.51 | 0.56 |
| Forests | 0.88 | 0.93 | 0.90 | 0.88 | 0.92 | 0.9 |
| Scrub and/or herbaceous vegetation associations | 0.74 | 0.69 | 0.72 | 0.73 | 0.67 | 0.7 |
| Open spaces with little or no vegetation | 0.87 | 0.84 | 0.85 | 0.85 | 0.82 | 0.84 |
| Inland wetlands | 0.81 | 0.78 | 0.80 | 0.82 | 0.77 | 0.79 |
| Maritime wetlands | 0.74 | 0.76 | 0.75 | 0.87 | 0.89 | 0.88 |
| Inland waters | 0.94 | 0.92 | 0.93 | 0.94 | 0.91 | 0.92 |
| Marine waters | 0.98 | 0.99 | 0.98 | 0.97 | 0.98 | 0.98 |
| Class | Precision | Recall | F1 Score |
| Artificial surfaces | 0.85 | 0.81 | 0.83 |
| Agricultural areas | 0.90 | 0.91 | 0.91 |
| Forest and semi natural areas | 0.91 | 0.92 | 0.92 |
| Wetlands | 0.77 | 0.70 | 0.73 |
| Water bodies | 0.96 | 0.97 | 0.96 |
Sample results
Here are a few results from the model. To view more, see this story.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::land-cover-classification-sentinel-2" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=e732ee81a9c14c238a14df554a8e3225", "landingPage": "https://uneca.africageoportal.com/content/esri::land-cover-classification-landsat-8", "title": "Land Cover Classification (Landsat 8)", "description": "Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Raster, mosaic dataset, or image service. (Preferred cell size is 30 meters.)
Classified raster with the same classes as in the National Land Cover Database (NLCD) 2016.
Note: The classified raster contains 20 classes based on a modified Anderson Level II classification system as used by the National Land Cover Database.
This model has an overall accuracy of 77 percent. The table below summarizes the precision, recall and F1-score of the model on the validation dataset.
| Class | Collection 2 Level 2 Imagery | Collection 1 Level 1 Imagery | ||||
| Precision | Recall | F1 Score | Precision | Recall | F1 Score | |
| Open Water | 0.96 | 0.97 | 0.96 | 0.95 | 0.97 | 0.96 |
| Perennial Snow/Ice | 0.86 | 0.69 | 0.77 | 0.49 | 0.94 | 0.64 |
| Developed, Open Space | 0.51 | 0.38 | 0.44 | 0.43 | 0.38 | 0.4 |
| Developed, Low Intensity | 0.52 | 0.46 | 0.49 | 0.47 | 0.48 | 0.47 |
| Developed, Medium Intensity | 0.54 | 0.5 | 0.52 | 0.49 | 0.54 | 0.51 |
| Developed, High Intensity | 0.67 | 0.54 | 0.6 | 0.55 | 0.68 | 0.61 |
| Barren Land | 0.76 | 0.59 | 0.66 | 0.6 | 0.77 | 0.68 |
| Deciduous Forest | 0.74 | 0.81 | 0.78 | 0.78 | 0.76 | 0.77 |
| Evergreen Forest | 0.77 | 0.82 | 0.79 | 0.8 | 0.82 | 0.81 |
| Mixed Forest | 0.56 | 0.47 | 0.51 | 0.5 | 0.53 | 0.51 |
| Shrub/Scrub | 0.82 | 0.82 | 0.82 | 0.84 | 0.81 | 0.83 |
| Herbaceous | 0.78 | 0.74 | 0.76 | 0.79 | 0.77 | 0.78 |
| Hay/Pasture | 0.7 | 0.74 | 0.72 | 0.67 | 0.75 | 0.71 |
| Cultivated Crops | 0.87 | 0.91 | 0.89 | 0.91 | 0.9 | 0.9 |
| Woody Wetlands | 0.7 | 0.68 | 0.69 | 0.67 | 0.68 | 0.68 |
| Emergent Herbaceous Wetlands | 0.72 | 0.54 | 0.62 | 0.54 | 0.61 | 0.57 |
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::land-cover-classification-landsat-8" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=eafdf746e14b4eda8887bab8e59fd27c", "landingPage": "https://uneca.africageoportal.com/content/esri::human-settlements-classification-sentinel-2", "title": "Human Settlements Classification (Sentinel-2)", "description": "Human settlements maps are useful in understanding growth patterns, population distribution, resource management, change detection, and a variety of other applications where information related to earth surface is required. Human settlements classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
Raster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)
Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).
Output
Classified raster containing two classes: settlement and other.
Applicable geographies
This model is expected to work well in Europe.
Model architecture
This model uses the UNet model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an overall accuracy of 94.1 percent.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::human-settlements-classification-sentinel-2" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f7754e9617b84356845e5f877d3c36c6", "landingPage": "https://uneca.africageoportal.com/content/esri::human-settlements-classification-landsat-8", "title": "Human Settlements Classification (Landsat 8)", "description": "Human settlement maps are useful in understanding growth patterns, population distribution, resource management, change detection, and a variety of other applications where information related to earth surface is required. Human settlements classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
Raster, mosaic dataset, or image service. (Preferred cell size is 30 meters.)
This model has an overall accuracy of 91.6 percent.
Training data
This model has been trained on an Esri proprietary human settlements classification dataset.
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::human-settlements-classification-landsat-8" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=1e1ec9602f4743108708ccdf362e3c48", "landingPage": "https://uneca.africageoportal.com/content/esri::cloud-mask-generation-sentinel-2", "title": "Cloud Mask Generation (Sentinel-2)", "description": "This model is expected to work well in Europe and the United States. This model works well for land based areas. Large water bodies such as ocean, seas and lakes should be avoided.
Model architecture
This model uses the UNet model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an overall accuracy of 94 percent with L2A imagery. The table below summarizes the precision, recall and F1-score of the model on the validation dataset. The comparatively low precision, recall and F1 score for Low density clouds might cause false detection of such clouds in certain urban areas. Also, for certain seasonal clouds some extremely bright pixels might be missed out.
| Class | Precision | Recall | F1 score |
| High density | 0.960 | 0.975 | 0.968 |
| Medium density | 0.905 | 0.897 | 0.901 |
| Low density | 0.774 | 0.571 | 0.657 |
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::cloud-mask-generation-sentinel-2" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=979cb0cf938946bfb8bb2f41cf9f9795", "landingPage": "https://uneca.africageoportal.com/content/esri::building-footprint-extraction-africa", "title": "Building Footprint Extraction - Africa", "description": "This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.
Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.
Using the model
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/content/esri::building-footprint-extraction-africa" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d1598feb7ec64101b53b96d1879f8b22&sublayer=0", "landingPage": "https://uneca.africageoportal.com/datasets/d1598feb7ec64101b53b96d1879f8b22_0", "title": "OpenStreetMap Leisure Areas for Africa", "description": "This feature layer provides access to OpenStreetMap (OSM) leisure areas data for Africa, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM polygon (closed way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes leisure features defined as a query against the hosted feature layer (i.e. leisure is not blank).Retirement Notice: This item is in mature support as of August 2026 and will retire in December 2027. Similar items are available in this ArcGIS Online Group. Esri and WorldPop recommend updating your maps and apps to use the new version.
This layer contains WorldPop's 1-km resolution annual estimates of population density from the year 2000 to 2020. The estimates in this layer use WorldPop's Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based model and converted these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations.
The DOI for the original WorldPop.org population density data is 10.5258/SOTON/WP00674.
Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Population_Density_1km/ImageServer, which was acquired from https://www.worldpop.org/doi/10.5258/SOTON/WP00674 on 15 Sep, 2021.
", "keyword": [ "WorldPop", "Population Density", "Dasymetric", "worldpop_soton", "retiring-2027-12" ], "issued": "2021-11-03T22:11:07.000Z", "modified": "2026-08-10T18:35:03.000Z", "publisher": { "name": "WorldPop" }, "contactPoint": { "@type": "vcard:Contact", "fn": "WorldPop_Project", "hasEmail": "mailto:noreply@worldpop.org" }, "accessLevel": "public", "spatial": "-180.0000,-59.4921,179.9917,83.6329", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/WorldPop::worldpop-population-density-2000-2020-1km" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Population_Density_1km/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=56eb0f050c61434782f008a08331d23a", "landingPage": "https://uneca.africageoportal.com/datasets/WorldPop::worldpop-total-population-2000-2020-1km", "title": "WorldPop Total Population 2000-2020 1km", "description": "Retirement Notice: This item is in mature support as of August 2026 and will retire in December 2027. Similar items are available in this ArcGIS Online Group. Esri and WorldPop recommend updating your maps and apps to use the new version.
This layer contains WorldPop's 1-km resolution annual estimates of total population from the year 2000 to 2020. WorldPop estimated the total population for each cell in this data using a random forest-based dasymetric redistribution model. The estimates in this layer use WorldPop's Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based model. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations.
The DOI for the original WorldPop.org total population data is 10.5258/SOTON/WP00647.
Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_1km/ImageServer, which was acquired from https://www.worldpop.org/doi/10.5258/SOTON/WP00647 on 15 Sep, 2021.
", "keyword": [ "WorldPop", "Population", "Dasymetric", "worldpop_soton", "retiring-2027-12" ], "issued": "2021-11-03T21:19:47.000Z", "modified": "2026-08-10T18:34:20.000Z", "publisher": { "name": "WorldPop" }, "contactPoint": { "@type": "vcard:Contact", "fn": "WorldPop_Project", "hasEmail": "mailto:noreply@worldpop.org" }, "accessLevel": "public", "spatial": "-180.0000,-72.0004,180.0000,84.0079", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://uneca.africageoportal.com/datasets/WorldPop::worldpop-total-population-2000-2020-1km" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_1km/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=5826b14592ab4ebc99574919165bd860", "landingPage": "https://uneca.africageoportal.com/datasets/esri::world-bioclimates", "title": "World Bioclimates", "description": "Temperature Description | Growing Degree Days |
Very Hot | 9,000 – 13,500 |
Hot | 7,000 – 9,000 |
Warm | 4,500 – 7,000 |
Cool | 2,500 – 4,500 |
Cold | 1,000 – 2,500 |
Very Cold | 300 – 1,000 |
Arctic | 0 - 300 |
Aridity Description | Aridity Index |
Very Wet | 1.5 – 70 |
Wet | 1.0 – 1.5 |
Moist | 0.6 – 1.0 |
Semi-dry | 0.3 – 0.6 |
Dry | 0.1 – 0.3 |
Very Dry | 0.01 – 0.1 |
This work is licensed under the Esri Master License Agreement. Sectional title scheme involves ownership of common property in undivided shares. The owners in a sectional title development collectively form a body corporate responsible for the management and maintenance of the property.
Solid waste drop-offs provide an estimate of the locations of solid waste drop-off facilities within the City of Cape Town boundary. These facilities serve as public or municipal sites where residents and businesses can dispose of various types of solid waste, including general household refuse, recyclables and garden waste.
The file contains the landfill sites and refuse transfer stations attribute information in the City of Cape Town and the status of each. The refuse transfer stations act as a temporary holding areas for waste before it's transported to recycling centers or landfill.
Think twice is a recycling project for dry waste that has been rolled out in some parts of the City of Cape Town. Residents in these areas are encouraged to separate their recyclable materials (like paper, plastic, glass, and cans) into a designated bag for collection, alongside their regular waste. This initiative aims to promote waste reduction and resource recovery within the city.
This dataset shows the location of community centres in the City of Cape Town. These centres serve as important public facilities that support a wide range of social, educational, cultural, and recreational activities. They often host community meetings, training sessions, youth programs, and outreach services, and may also function as emergency support hubs during times of crisis.
For wetland representation\nand prioritization for inclusion as aquatic features in the City's Biodiversity\nNetwork. To be used in conjunction with the terrestrial Biodiversity Network\nand rivers layers (Ecological Classification and Buffer Information from 2002).
Users of the data are\nrequired to verify the information by visiting the site with a biodiversity\nspecialist and cannot rely on the data as being accurate, despite our best\nefforts.
All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.
Scenic routes facilitate appreciation of Cape Town's natural, built and cultural heritage, and in themselves have become attractions. (2012) Where applicable, relevant Land Use requirements are introduced adjacent to these routes.
Scenic Routes have been categorised into 2 types:- SR1 routes are limited-access routes that traverse areas of high scenic quality (e.g. Chapman's Peak);- SR2 routes traverse areas of high scenic quality and are frequently accessed (e.g. Hout Bay Main Road).
All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.
(Storm water Open Watercourses (OWC): rivers identification information) The status of the official Open Watercourse layer is representative of natural and built river systems, which integrates surface storm water runoff into underground urban drainage reticulation, from upstream mountain intakes to coastal outfalls. Rain-water is routed along natural flow paths, via environmental wetland eco-systems and artificial storm detention/retention depressions, through existing streams and tributaries of the bulk open natural and built-infrastructure river systems. The Open Watercourses are intercepted by Inlet-Outlet infrastructure, which deposits storm water urban runoff into underground storm water drainage reticulation networks, then converges with the road drainage runoff taken into catch pits from road surface channels into the underground reticulation, by means of the above open/closed systems, towards coastal outfalls.
(OWC: spatial vector / non-spatial (attribute or tabular) / spatial extents)Open Watercourse river reaches are comprised of feature types such as Natural Rivers, Streams, Channels, Canals, Overland Flow Paths/Gap Connectors and Stream Extensions. The spatial coverage of Open Watercourses of the greater Cape Town Drainage Catchment area includes all rivers within the catchments, which drain into but also extend outside the Cape Town metro area.
All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.
To indicate\nlocations of Fire Stations within the City of Cape Town.
All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.
This dataset represents the ITPS (Informal Trading Permitting System) trading bays for the City of Cape Town captured by the Geomatics section (IS&T Department) on behalf of the custodian, Economic Development Department.
The dataset was generated by incorporating the City of Cape Town buildings data from SAP, the intranet telephone book (CityPhone), the MS Outlook phone book and the IPARA list into the existing legacy feature class for City of Cape Town buildings. This dataset was verified by using Aerial Photography, Oblique imagery and Google Street view where possible. This dataset has not been field verified.
This dataset represents the locations of all City of Cape Town buildings in the City of Cape Town.The main building types covered are: Offices, Cash Offices, Cemeteries, Clinics, Depots, Fire Stations, Halls, Law Enforcement, Libraries, Municipal Pools, Reservoirs, Sport Facilities, Traffic Departments, Workshops, etc.
All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.Analysis
Analysis
Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.
Analysis
This work is licensed under the Esri Master License Agreement. What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.
Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.
Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter.
Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal
",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://uneca.africageoportal.com/maps/4e681ff69e0e4b90866bb6a2e03db24a"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=37a875ff3611496883b7ccca97f0f5f4",
"landingPage": "https://uneca.africageoportal.com/maps/37a875ff3611496883b7ccca97f0f5f4",
"title": "{{name}}",
"description": "{{description}}",
"keyword": [
"NOAA",
"GOES",
"satellite",
"weather",
"disaster",
"easrth observation",
"Himawari",
"infrared",
"imagery"
],
"issued": "2020-09-18T20:15:58.000Z",
"modified": "{{modified:toISO}}",
"publisher": {
"name": "{{source}}"
},
"contactPoint": {
"@type": "vcard:Contact",
"fn": "VizLab_noaa",
"hasEmail": "{{orgContactEmail}}"
},
"accessLevel": "public",
"spatial": "",
"license": "Public Domain These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://uneca.africageoportal.com/maps/37a875ff3611496883b7ccca97f0f5f4"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=8e93e0f942ae4d54a8d089e3cd5d2774",
"landingPage": "https://uneca.africageoportal.com/maps/8e93e0f942ae4d54a8d089e3cd5d2774",
"title": "{{name}}",
"description": "{{description}}",
"keyword": [
"NOAA",
"satellite",
"weather",
"disaster",
"earth observations",
"GOES",
"Himawari",
"infrared",
"imagery"
],
"issued": "2019-06-26T18:24:55.000Z",
"modified": "{{modified:toISO}}",
"publisher": {
"name": "{{source}}"
},
"contactPoint": {
"@type": "vcard:Contact",
"fn": "VizLab_noaa",
"hasEmail": "{{orgContactEmail}}"
},
"accessLevel": "public",
"spatial": "",
"license": "Public domain These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://uneca.africageoportal.com/maps/8e93e0f942ae4d54a8d089e3cd5d2774"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=255af1ceee844d6da8ef8440c8f90d00",
"landingPage": "https://uneca.africageoportal.com/datasets/esri::sentinel-2-level-2a",
"title": "Sentinel-2 Level-2A",
"description": "Sentinel-2 multispectral and multitemporal atmospherically corrected imagery with on-the-fly visual renderings and indices for visualization and analysis. This includes thirteen multispectral bands at spatial resolutions of 10, 20, and 60-meters. This Imagery Layer is sourced from the Planetary Computer Sentinel-2 Level-2A data catalog on Azure, which is updated daily with the latest available imagery.Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Multispectral Bands
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
Additional Usage Notes
Data Source
Sentinel-2 imagery is the result of close collaboration between the European Space Agency (ESA), the European Commission and the USGS. The source data for this service is processed by Esri and hosted in Azure as as part of the Planetary Computer Data Catalog.
For information on Sentinel-2 imagery, see Sentinel-2.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.