  Regularly updated and accurate land cover maps are essential for monitoring
14 of the 17 Sustainable Development Goals. Multispectral satellite imagery
provide high-quality and valuable information at global scale that can be used
to develop land cover classification models. However, such a global application
requires a geographically diverse training dataset. Here, we present
LandCoverNet, a global training dataset for land cover classification based on
Sentinel-2 observations at 10m spatial resolution. Land cover class labels are
defined based on annual time-series of Sentinel-2, and verified by consensus
among three human annotators.
