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

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 t...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Quinton, Félix, Landrieu, Loic
Format: Article
Language:English
Subjects:
Classification
Deep learning
Machine learning
Modelling
Optimization
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422