Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series release_gqa6keupufcjdcxymo5n4sd5dy

by Félix Quinton, loic landrieu

Published in Remote Sensing by MDPI AG.

2021   Volume 13, Issue 22, p4599

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 to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.
In application/xml+jats format

Archived Files and Locations

application/pdf  10.3 MB
file_e6xj4zb4vjdqbhpmcqb45w4ew4
mdpi-res.com (publisher)
web.archive.org (webarchive)

Web Captures

https://www.mdpi.com/2072-4292/13/22/4599/htm
2022-08-25 23:41:33 | 54 resources
webcapture_rddbe7uvmfdr7ed6w53r3kculi
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-11-16
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2072-4292
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: ab4d10f3-4098-48d4-b7a3-cf4a7b21f86b
API URL: JSON