Automated Annual
Global Maps

Accurate, timely, global maps released openly for maximum impact.

Our Land Use & Land Cover Maps

Leaders in governments, NGOs, finance and industry need trustworthy, actionable information about the changing world to understand opportunities, identify threats, and measure the impacts of actions. Our Land Use & Land Cover maps meet this need with timely, up-to-date maps at previously unobtainable scale.


Through our team's work, you can access annual global land use & land cover maps, based on 10m Sentinel-2 imagery.

Land Use Land Cover map


“Land cover maps sit at the core of everything we are going to have to do moving forward. It’s the foundational dataset to any nervous system for sustainability on the planet.”


- Lucas Joppa, Microsoft’s Chief Environmental Officer on stage at the Esri Imagery Summit 2021, Redlands, CA

IO team working on Land Use Land Cover map

We created these maps in partnership with Esri and Microsoft, and have made them available under an open license (CC BY 4.0) You can access these maps on Esri’s Living Atlas and Planetary Computer.


Discover how we leveraged Microsoft’s Azure to create each of these maps in less than a week.

Behind the Scenes

  • This dataset was generated using billions of human-labeled pixels to train a deep learning model for land classification.
  • The global map was produced by applying this model to the relevant yearly ESA Sentinel-2 satellite imagery.
  • Consistent mapping across the globe based on IO’s robust AI algorithms.

Land Use Land Cover map


9-class
LULC

Annual global estimates of 9-class land use/land cover for 2017-2021, derived from ESA Sentinel-2 imagery at 10m resolution.


85% overall
accuracy

Industry-leading multi-category maps with 85% overall accuracy.

Read our Methodology & Accuracy Summary for more information.

LULC Class Definitions

Water

Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.

Trees

Any significant clustering of tall (~15ft / 5m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).

Rangeland

Any area of low, non-flooded vegetation with very little-to-no taller (~15ft / 5m or higher) vegetation, homogeneous or heterogeneous, containing any degree of the following: wild cereals and grasses with no obvious human plotting (i.e. not a plotted field); mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; clearings of homogeneous grasses; scrub-filled clearings within dense forests that are clearly not taller than trees.

Flooded Vegetation

Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.

Crops

Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.

Built Area

Human made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages/towns/cities, paved roads, asphalt.

Bare Ground

Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.

Snow/Ice

Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.

Clouds

No land cover information due to persistent cloud cover.


K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby, "Global land use / land cover with Sentinel 2 and deep learning," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 4704-4707, doi: 10.1109/IGARSS47720.2021.9553499.