LandCoverNet: A global benchmark land cover classification training dataset
This dataset addresses the need for geographically diverse training data to develop global land cover classification models, which are crucial for monitoring 14 of the 17 Sustainable Development Goals.
The authors created LandCoverNet, a global training dataset for land cover classification using 10m spatial resolution Sentinel-2 observations. The dataset's land cover class labels were defined from annual Sentinel-2 time-series and verified by consensus among three human annotators.
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.