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

Félix Quinton (1) , Loic Landrieu (2)

1 ENSG - École nationale des sciences géographiques
2 LaSTIG - Laboratoire sciences et technologies de l'information géographique

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.

Domaines: Machine Learning [stat.ML]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Félix Quinton, Loic Landrieu. Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series. Remote Sensing, 2021, 13 (22), pp.4599. ⟨10.3390/rs13224599⟩. ⟨hal-03500526⟩