  We introduce OpenEarthMap, a benchmark dataset, for global high-resolution
land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000
aerial and satellite images covering 97 regions from 44 countries across 6
continents, with manually annotated 8-class land cover labels at a 0.25--0.5m
ground sampling distance. Semantic segmentation models trained on the
OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a
variety of applications. We evaluate the performance of state-of-the-art
methods for unsupervised domain adaptation and present challenging problem
settings suitable for further technical development. We also investigate
lightweight models using automated neural architecture search for limited
computational resources and fast mapping. The dataset is available at
https://open-earth-map.org.
