  Monitoring of land cover and land use is crucial in natural resources
management. Automatic visual mapping can carry enormous economic value for
agriculture, forestry, or public administration. Satellite or aerial images
combined with computer vision and deep learning enable precise assessment and
can significantly speed up change detection. Aerial imagery usually provides
images with much higher pixel resolution than satellite data allowing more
detailed mapping. However, there is still a lack of aerial datasets made for
the segmentation, covering rural areas with a resolution of tens centimeters
per pixel, manual fine labels, and highly publicly important environmental
instances like buildings, woods, water, or roads.
  Here we introduce LandCover.ai (Land Cover from Aerial Imagery) dataset for
semantic segmentation. We collected images of 216.27 sq. km rural areas across
Poland, a country in Central Europe, 39.51 sq. km with resolution 50 cm per
pixel and 176.76 sq. km with resolution 25 cm per pixel and manually fine
annotated four following classes of objects: buildings, woodlands, water, and
roads. Additionally, we report simple benchmark results, achieving 85.56% of
mean intersection over union on the test set. It proves that the automatic
mapping of land cover is possible with a relatively small, cost-efficient,
RGB-only dataset. The dataset is publicly available at
https://landcover.ai.linuxpolska.com/
