Geolocation-Aware Land Cover Classification from Sentinel-2 Images

Accurate land cover maps provide crucial information for various purposes. Due to their global accessibility, and high temporal, spectral, and spatial resolution, remote sensing images are a popular source to extract and update land cover maps. However, a reliable and effective global model that inputs satellite images and outputs accurate land cover class for each pixel is complex to achieve. The reason is the varying characteristics and heterogeneous distribution of land cover classes over the globe. In this paper, we propose a geolocation-aware architecture to classify land covers globally using Sentinel-2 data. While the proposed method is trained with a global dataset, it takes the regional characteristics of the data into account by dedicating branches to each major climate zone. The Sentinel-2 images together with the corresponding ESA world cover labels from the SEN12TP dataset are used to train the network. Our proposed model is compared against a globally trained UNet, and the results reveal faster convergence and enhanced visual predictions.