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 to model simultaneously the inter- and intra-annual agricu...

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Autores principales:	Félix Quinton, Loic Landrieu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	crop mapping crop rotation Sentinel-2 Science Q
Acceso en línea:	https://doaj.org/article/3708c8d3953d49599511664b065a24b9
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id	oai:doaj.org-article:3708c8d3953d49599511664b065a24b9
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spelling	oai:doaj.org-article:3708c8d3953d49599511664b065a24b92021-11-25T18:54:39ZCrop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series10.3390/rs132245992072-4292https://doaj.org/article/3708c8d3953d49599511664b065a24b92021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4599https://doaj.org/toc/2072-4292While 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.Félix QuintonLoic LandrieuMDPI AGarticlecrop mappingcrop rotationSentinel-2ScienceQENRemote Sensing, Vol 13, Iss 4599, p 4599 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crop mapping crop rotation Sentinel-2 Science Q
spellingShingle	crop mapping crop rotation Sentinel-2 Science Q Félix Quinton Loic Landrieu Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
description	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.
format	article
author	Félix Quinton Loic Landrieu
author_facet	Félix Quinton Loic Landrieu
author_sort	Félix Quinton
title	Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
title_short	Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
title_full	Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
title_fullStr	Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
title_full_unstemmed	Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
title_sort	crop rotation modeling for deep learning-based parcel classification from satellite time series
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3708c8d3953d49599511664b065a24b9
work_keys_str_mv	AT felixquinton croprotationmodelingfordeeplearningbasedparcelclassificationfromsatellitetimeseries AT loiclandrieu croprotationmodelingfordeeplearningbasedparcelclassificationfromsatellitetimeseries
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Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

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