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Monitoring of land cover and land use is crucial in natural resourcesmanagement. Automatic visual mapping can carry enormous economic value foragriculture, forestry, or public administration. Satellite or aerial imagescombined with computer vision and deep learning enable precise assessment andcan significantly speed up change detection. Aerial imagery usually providesimages with much higher pixel resolution than satellite data allowing moredetailed mapping. However, there is still a lack of aerial datasets made forthe segmentation, covering rural areas with a resolution of tens centimetersper pixel, manual fine labels, and highly publicly important environmentalinstances like buildings, woods, water, or roads.  Here we introduce LandCover.ai (Land Cover from Aerial Imagery) dataset forsemantic segmentation. We collected images of 216.27 sq. km rural areas acrossPoland, a country in Central Europe, 39.51 sq. km with resolution 50 cm perpixel and 176.76 sq. km with resolution 25 cm per pixel and manually fineannotated four following classes of objects: buildings, woodlands, water, androads. Additionally, we report simple benchmark results, achieving 85.56% ofmean intersection over union on the test set. It proves that the automaticmapping of land cover is possible with a relatively small, cost-efficient,RGB-only dataset. The dataset is publicly available athttps://landcover.ai.linuxpolska.com/

LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery