derived data land cover machine learning open data raster sentinel hub
The 10m Annual Land Use Land Cover (LULC) map is produced Impact Observatory, Microsoft, and Esri collaboratively. The data collection is derived from ESA Sentinel-2 imagery at 10m resolution globaly using Impact Ovservatory's state of the art deep learning AI land classification model which is trained by billions of human-labeled image pixels. There are 9 LULC classes generated by the algorithm, including Built, Crops, Trees, Water, Rangeland, Flooded Vegetation, Snow/Ice, Bare Ground, and Clouds.
10m
Global land area
2017-2023
Annually, each January
| Name | Description |
|---|---|
| lulc | Main discrete classification defined in the product documentation. |
Collection of custom scripts for this dataset
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services.sentinel-hub.combyoc-0ed26381-7344-4281-b180-66f3da521f750ed26381-7344-4281-b180-66f3da521f75