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

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 the precise assessment and can significantly speed up the process of 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 that were made for the segmentation, covering rural area with resolution of tens centimeters per pixel, manual fine labels and highly publicly important environmental instances like buildings, woods or water. 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 three following classes of objects: buildings, woodlands, and water. Additionally, we report simple benchmark results, achieving 90.18% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible with relatively small, cost efficient, RGB only dataset. The dataset is publicly available at http://landcover.ai