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This work presents SeasoNet, a new large-scale multi-label land cover andland use scene understanding dataset. It includes $1\,759\,830$ images fromSentinel-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 scalepixel level labels from the German land cover model LBM-DE2018 with land coverclasses based on the CORINE Land Cover database (CLC) 2018 and a five timessmaller minimum mapping unit (MMU) than the original CLC maps. We provide pixelsynchronous examples from all four seasons, plus an additional snowy set. Theseproperties make SeasoNet the currently most versatile and biggest remotesensing scene understanding dataset with possible applications ranging fromscene classification over land cover mapping to content-based cross seasonimage retrieval and self-supervised feature learning. We provide baselineresults by evaluating state-of-the-art deep networks on the new dataset inscene classification and semantic segmentation scenarios.

SeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany