CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING

Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are...

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Main Authors: C. Mallet, A. Le Bris
Format: Article
Language:English
Published: Copernicus Publications 2020-08-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/703/2020/isprs-archives-XLIII-B2-2020-703-2020.pdf
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spelling doaj-0511ac033ee74a44a1d40f016efd6daf2020-11-25T03:39:14ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B2-202070371010.5194/isprs-archives-XLIII-B2-2020-703-2020CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPINGC. Mallet0A. Le Bris1Univ. Gustave Eiffel, IGN-ENSG, LaSTIG, Saint-Mandé, FranceUniv. Gustave Eiffel, IGN-ENSG, LaSTIG, Saint-Mandé, FranceMany land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/703/2020/isprs-archives-XLIII-B2-2020-703-2020.pdf
collection DOAJ
language English
format Article
sources DOAJ
author C. Mallet
A. Le Bris
spellingShingle C. Mallet
A. Le Bris
CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet C. Mallet
A. Le Bris
author_sort C. Mallet
title CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
title_short CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
title_full CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
title_fullStr CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
title_full_unstemmed CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING
title_sort current challenges in operational very high resolution land-cover mapping
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2020-08-01
description Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/703/2020/isprs-archives-XLIII-B2-2020-703-2020.pdf
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