Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

Abstract

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 6.6 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.