  While annual crop rotations play a crucial role for agricultural
optimization, they have been largely ignored for automated crop type mapping.
In this paper, we take advantage of the increasing quantity of annotated
satellite data to propose the first deep learning approach modeling
simultaneously the inter- and intra-annual agricultural dynamics of parcel
classification. Along with simple training adjustments, our model provides an
improvement of over 6.3 mIoU points over the current state-of-the-art of crop
classification. Furthermore, we release the first large-scale multi-year
agricultural dataset with over 300,000 annotated parcels.
