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Auteur Clément Mallet
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SenRVM: A multi-modal deep learning regression methodology for continuous vegetation monitoring with dense temporal NDVI time series / Anatol Garioud (2022)
Titre : SenRVM: A multi-modal deep learning regression methodology for continuous vegetation monitoring with dense temporal NDVI time series Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2022 Conférence : LPS 2022, ESA Living Planet Symposium 22/05/2022 27/05/2022 Bonn Allemagne programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] dynamique de la végétation
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) The Earth's biosphere and the phenology of vegetation are at the heart of climatic, economic and social concerns. Human activities have led to a significant degradation of ecosystem services (e.g. carbon sequestration, biodiversity, water quality, flood, and erosion regulation) provided by various extensive ecosystems such as forests, grasslands or crops.
A key parameter for relevant climate modeling, public policy implementations or commercial applications is the temporal resolution at which vegetation is observed. As a tool providing synoptic and regular coverage of Earth’s surfaces, satellite Earth Observation has been increasingly adopted, among others, for estimating biomass, yields, modeling different fluxes or detecting changes. Optical images have been historically used for vegetation monitoring, considering their efficient discrimination of phenomena related to photosynthetic activity.
To deal with missing data due to clouds, many interpolation strategies integrating one or more optical sensors have been developed. Most of these strategies are based on trend modelling that does not reflect the real evolution of the vegetation cover in many cases (sudden climatic impact, man-made effects). As a result, data that may be weeks or months apart are often interpolated on areas suffering from high cloud cover.
Copernicus Sentinels provide new opportunities and unprecedented observations for the monitoring of vegetation’s dynamics. In particular, concordant optical and SAR data sets provided by the Sentinel-1 and 2 satellites open the door to new multi-sensor methodologies aiming at the reconstruction of missing information.
Taking into account the still numerous non-cloudy observations provided by the Sentinel-2 satellites, a deep learning regression methodology, namely the Sentinels Regression for Vegetation Monitoring (SenRVM), has been developed. Its goal is the translation of SAR features acquired regardless of the climatic conditions into NDVI. The developed architecture integrates several deep learning architectures such as Multilayer Perceptron and Recurrent Neural Networks. The SenRVM regression strategy proposes the integration of auxiliary data such as climatic and topographic features. This allows accurate NDVI time series to be predicted by minimizing effects exogenous to the vegetation’s phenology through SAR acquisitions contextualization.
Object-oriented analysis of the results is carried out on large scale areas for various vegetation types with distinct phenologies (grasslands, crops and forests). The results are analyzed by taking into account spatial and temporal aspects or with an ablation study of the Network’s inputs. The proposed approach is further compared with traditional interpolation methods exploiting monomodal (Whittaker smoothing, linear weighted interpolation) or multimodal (Random Forest, Gaussian Regression Processes, single Multilayer Perceptron) features.
The potential of high-temporal NDVI time series obtained by the SenRVM method for several vegetation-related applications is subsequently illustrated. In particular, the interest of the obtained time series to observe the phenology and its associated parameters of the three main vegetation classes is presented.Numéro de notice : C2022-011 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100786 Documents numériques
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SenRVM - posterAdobe Acrobat PDF The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)
Titre : The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data : EuroSDR and LandSense Workshop, November 24th - 25th 2020, Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; Joep Crompvoets, Auteur ; Inian Moorthy, Auteur ; Clément Mallet , Auteur ; Bénédicte Bucher , Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR Workshop report Projets : Landsense / Raimond, Ana-Maria Conférence : VGI4LULC 2020, The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data 24/11/2020 25/11/2020 online Allemagne OA Proceedings Importance : 40 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] approche participative
[Termes IGN] cartographie collaborative
[Termes IGN] collecte de données
[Termes IGN] Corine Land Cover
[Termes IGN] détection de changement
[Termes IGN] données localisées des bénévoles
[Termes IGN] intégration de données
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] science citoyenne
[Termes IGN] utilisation du solRésumé : (éditeur) The report refers to the workshop that was organized on behalf of EuroSDR and the LandSense project (24-25 November 2020). LandSense aims to build a citizen observatory for Land Use and Land Cover (LULC) monitoring by proposing innovate technologies for data collection, change detection, data quality assessment and offering tools and systems to empower different communities (e.g., private companies, Non Governmental Organisation, National Mapping Agencies, research, public authorities) to monitor and report on LULC. The workshop was co-organized by the LASTIG laboratory of the University Gustave Eiffel and IGN-ENSG, the French National Mapping agency (Ana-Maria Olteanu-Raimond, Clément Mallet, Bénédicte Bucher), the Katholieke Universiteit Leuven (Joep Crompvoets), the International Institute for Applied Systems Analysis (Inian Moorthy) and EuroSDR. Note de contenu : INTRODUCTION GENERALE
1. Introduction
1.1 Land Use and Land Cover data: specificities and challenges
1.2 VGI and citizen science for LULC monitoring
2. Session 1: Use of VGI for LULC data production
2.1 National Land Cover and Land Use Information System of Spain (SIOSE)- Coordination,
production, maintenance and VGI
2.2 A fusion data approach for integrating VGI to update and enrich authoritative LULC data
2.3 OpenStreetMap for Earth Observation (OSM4EO) land use application and benchmark
2.4 Using OpenStreetMap as a data source for training classifiers to generate LULC maps
3. Session 2: Data collection and validation
3.1 A mapping prototype for land use mapping by land users
3.2 A mobile application for collecting snow data in support to satellite remote sensing
3.3 Global land cover monitoring, validation and participation: experiences from several case studies
4. Session 3: Sustainability
4.1 Crowdsourcing reference data collection for land cover and land use mapping: Findings from Picture Pile and FotoquestGo
4.2 Land Cover Monitoring System with Sentinel-Hub and Python Machine Learning Library eo-learn. Is it possible to build a fast and cost-effective LCMS?
4.3 Regular monitoring of landscape changes with Copernicus data- The German land cover change detection service
4.4 Authentication as a Service - A LandSense contribution to improve the FAIR principle in Citizen Science
5. ConclusionNuméro de notice : 28680 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_vgi4lulc.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99973 Fast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)
[article]
Titre : Fast estimation for robust supervised classification with mixture models Type de document : Article/Communication Auteurs : Erwan Giry Fouquet, Auteur ; Mathieu Fauvel, Auteur ; Clément Mallet , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : MAESTRIA / Mallet, Clément, ANITI / Mallet, Clément Article en page(s) : pp 320 - 326 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] échantillon
[Termes IGN] méthode robuste
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) Label noise is known to negatively impact the performance of classification algorithms. In this paper, we develop a model robust to label noise that uses both labelled and unlabelled samples. In particular, we propose a novel algorithm to optimize the model parameters that scales efficiently w.r.t. the number of training samples. Our contribution relies on a consensus formulation of the original objective function that is highly parallelizable. The optimization is performed with the Alternating Direction Method of Multipliers framework. Experimental results on synthetic datasets show an improvement of several orders of magnitude in terms of processing time, with no loss in terms of accuracy. Our method appears also tailored to handle real data with significant label noise. Numéro de notice : A2021-061 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.patrec.2021.10.020 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1016/j.patrec.2021.10.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99531
in Pattern recognition letters > vol 152 (December 2021) . - pp 320 - 326[article]Investigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)
[article]
Titre : Investigating operational country-level crop monitoring with Sentinel~1 and~2 imagery Type de document : Article/Communication Auteurs : Nicolas David , Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 970 - 982 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] chaîne de traitement
[Termes IGN] France (administrative)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] politique agricole commune
[Termes IGN] surveillance agricoleRésumé : (auteur) In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) umbrella. Our pipeline is based on the ι2 open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2 optical images, with complementary contributions. Three conceivable scenarios are investigated with two sets of nomenclatures (17 and 43 classes): early, on-line, and late classifications. Experiments performed on 2017 show very satisfactory results (82–97%), locally almost on-par with state-of-the-art deep-based methods. We can conclude our framework offers a strong basis for country-scale operational deployment for 2020+CAP. Numéro de notice : A2021-600 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/2150704X.2021.1950940 En ligne : https://doi.org/10.1080/2150704X.2021.1950940 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98222
in Remote sensing letters > vol 12 n° 10 (October 2021) . - pp 970 - 982[article]Recurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)
[article]
Titre : Recurrent-based regression of Sentinel time series for continuous vegetation monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : 3-projet - voir note / Mallet, Clément Article en page(s) : n° 112419 Note générale : bibliographie
This work is funded by the Agence de la transition écologique (ADEME) and the Centre National d'Études Spatiales (CNES).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Dense time series of optical satellite imagery describing vegetation activity provide essential information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal resolution of optical sensors is strongly affected by cloud cover, resulting in significant missing information. The use of complementary acquisitions, such as Synthetic Aperture Radar (SAR) data, opens the door to the development of new multi-sensor methodologies aiming at the reconstruction of missing information. However, the joint exploitation of new radar and optical missions, such as the Sentinel, raises new challenges given the different nature and response of the two data sources. In this work, the SenRVM methodology is proposed as a new multi-sensor approach to regress SAR time series towards Normalized Difference Vegetation Index (NDVI). A deep Recurrent Neural Network architecture which integrates SAR acquisitions and ancillary data is adopted. The regression task permits a continuous optical temporal resolution of 6 days. Multiple experiments are carried out to assess the SenRVM framework by studying two large-scale areas in France. Through an extensive interpretation of the results, SenRVM is evaluated on three main vegetation types (grasslands, crops, and forests). High accurate results (R2 > 0.83 and MAE Numéro de notice : A2021-499 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2021.112419 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98004
in Remote sensing of environment > vol 263 (15 September 2021) . - n° 112419[article]vol V-1-2021 - July 2021 - [Actes] XXIV ISPRS Congress, Commission 1 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkvol V-2-2021 - July 2021 - [Actes] XXIV ISPRS Congress, Commission 2 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkvol V-4-2021 - July 2021 - [actes] XXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission 4, 2021 edition, 5–9 July 2021 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkPreface: the 2021 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)PermalinkToward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)PermalinkAn efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkCombining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)PermalinkPermalinkPermalink
Senior researcher in LaSTIG & head of LaSTIG
HDR defense in 2016
Page perso : https://sites.google.com/view/clementmallet