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Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
[article]
2021
arXiv
pre-print
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
arXiv:2110.08187v2
fatcat:imedafp3zfdjtc7t47zwilh564