LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018.
https://doi.org/10.34911/rdnt.63fxe5LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Europe contains data across Europe, which accounts for ~9.5% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice. There are a total of 840 image chips of 256 x 256 pixels in LandCoverNet Europe V1.0 spanning 28 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
Radiant Earth Foundation (2022) LandCoverNet Europe: A Geographically Diverse Land Cover Classification Training Dataset, Version 1.0, Radiant MLHub. https://doi.org/10.34911/rdnt.7s12zu