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 datasets that were made for the segmentation of buildings with other highly publicly important environmental instances like woods or water. Here we introduce LandCover.ai (Land Cover from Aerial Imagery) dataset that propose semantic segmentation. We collected images of 216.27 sq. km lands 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 86.2% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible and can be applied in various domains. The dataset is publicly available at http://landcover.ai