  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.
