Seasonet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery Over Germany

• Published in: IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 243 - 246
• Main Authors: Kobmann, Dominik, Brack, Viktor, Wilhelm, Thorsten
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.07.2022
• Subjects: dataset; Image analysis; Image segmentation; land cover classification; mapping; Protocols; Representation learning; retrieval; Satellites; seasonal changes; Self-supervised learning; Semantics
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS46834.2022.9884079

This work presents SeasoNet, a new large-scale multi-label land cover and land use scene understanding dataset. It includes 1759830 images from Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to 120 \text{px}\times 120{\text{px}} . Each image is annotated with large scale pixel level labels from the german land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018 and a five times smaller minimum mapping unit (MMU) than the original CLC maps. We provide pixel synchronous examples from all four seasons, plus an additional snowy set. These properties make SeasoNet the currently most versatile and biggest remote sensing scene understanding dataset with possible applications ranging from scene classification over land cover mapping to content-based cross season image retrieval and self-supervised feature learning. We provide base-line results by evaluating state-of-the-art deep networks on the new dataset in scene classification and semantic segmentation scenarios.