SeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany
This dataset addresses the need for versatile and large remote sensing data for tasks like land cover mapping and cross-season image retrieval, but it is incremental as it builds on existing land cover models and datasets.
The authors introduced SeasoNet, a large-scale multi-label dataset for satellite imagery over Germany, containing 1,759,830 Sentinel-2 images with pixel-level annotations from land cover models, and provided baseline results using state-of-the-art deep networks for scene classification and semantic segmentation.
This work presents SeasoNet, a new large-scale multi-label land cover and land use scene understanding dataset. It includes $1\,759\,830$ images from Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to $ 120 \ \mathrm{px} \times 120 \ \mathrm{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 baseline results by evaluating state-of-the-art deep networks on the new dataset in scene classification and semantic segmentation scenarios.