2020 Global Land Use Data

Caitlin Dempsey


A global land cover GeoTIFF was recently released by Impact Observatory (IO) and Esri. To create this geospatial layer, hundreds of thousands of satellite photos were classified into ten unique land use/land cover (LULC) classes using a deep learning model in partnership with Microsoft AI for Earth.

Sentinel-2 imagery was used to divide the world into ten categories of land use cover:

  • Water (areas that are predominately water such as rivers ponds, lakes, and ocean)
  • Trees (clusters that are at least 10 meters high)
  • Grasslands such as open savannas, parks, and golf courses
  • Flooded vegetation such as wetlands, rice paddies, and
  • Crops
  • Scrubland
  • Built areas such as urban/suburban, highways, railways, and paved areas.
  • Bare ground in areas with little or no vegetation such as exposed rock/soil and sparsely vegetated deserts.
  • Permanent snow and ice areas
  • Cloud cover areas where the persistent cloud cover prevents an analysis of the underlying land cover.

The end product is a 10-meter resolution GeoTiff that the developers have released under a Creative Commons 4.0 license. The machine learning model was run on multiple dates throughout the year with the results folded into one consolidated layer to represent land use cover for the year 2020.

Screenshot showing the Esri 2020 Land Cover.
Screenshot showing the Esri 2020 Land Cover. Acquired June 24, 2021.

The 2020 Esri Land Cover dataset can be browsed using Esri’s online Map Viewer. Users can also access the full global GeoTIFF zip file by using Esri’s tool for accessing the land use data by tile.

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Learn more

Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail. Medium. https://caitlin-kontgis.medium.com/mapping-the-world-in-unprecedented-detail-7c0513205b90

Using the Esri Land Cover Data in QGIS

If you are interested in using Esri’s recently released land cover GIS data in QGIS, Abdul Raheem Siddiqui has step-by-step instructions on how to access the layer using GDAL Cloud Optimized GeoTiffs in QGIS.


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About the author
Caitlin Dempsey
Caitlin Dempsey is the editor of Geography Realm and holds a master's degree in Geography from UCLA as well as a Master of Library and Information Science (MLIS) from SJSU.