This paper introduces the MapInWild dataset, a multi-modal dataset tailored to mapping wilderness areas from satellite imagery and auxiliary geodata. MapInWild accommodates freely and globally available geodata layers that emerged from various remote sensing sensors, such as dualpol Sentinel-1 imagery, multi-spectral Sentinel-2 data, Visible Infrared Imaging Radiometer Suite night-time light data, and the ESA WorldCover map. Each sample of the Map-InWild dataset is annotated with labels derived from the World Database on Protected Areas, a most up-to-date and comprehensive global database on conservation areas. Protected areas are filtered through a sophisticated sampling process to ensure a representative coverage of the natural areas of the Earth. With MapInWild dataset, we hope to foster further research on deep learning applied to environmental remote sensing and conservation. MapInWild dataset is publicly available at https://dataverse.harvard.edu/dataverse/mapinwild.     «

This paper introduces the MapInWild dataset, a multi-modal dataset tailored to mapping wilderness areas from satellite imagery and auxiliary geodata. MapInWild accommodates freely and globally available geodata layers that emerged from various remote sensing sensors, such as dualpol Sentinel-1 imagery, multi-spectral Sentinel-2 data, Visible Infrared Imaging Radiometer Suite night-time light data, and the ESA WorldCover map. Each sample of the Map-InWild dataset is annotated with labels derived...    »