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Auteur Clément Mallet
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International workshop on large scale land cover mapping from remote sensing, 3 décembre 2019 / Mathieu Fauvel (2019)
Titre : International workshop on large scale land cover mapping from remote sensing, 3 décembre 2019 Type de document : Actes de congrès Auteurs : Mathieu Fauvel, Organisateur de réunion ; Jordi Inglada, Organisateur de réunion ; Arnaud Le Bris , Organisateur de réunion ; Clément Mallet , Organisateur de réunion Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : MAESTRIA / Mallet, Clément Conférence : International workshop 2019 on large scale land cover mapping from remote sensing 03/12/2019 03/12/2019 Saint-Mandé France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] base de données d'occupation du sol
[Termes IGN] image numérique
[Termes IGN] occupation du sol
[Termes IGN] territoireRésumé : Workshop sans actes organisé dans le cadre du projet MAESTRIA Numéro de notice : 14366 Affiliation des auteurs : LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96917 Joint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)
Titre : Joint analysis of SAR and optical satellite images time series for grassland event detection Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] cohérence des données
[Termes IGN] détection d'événement
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] prairie
[Termes IGN] puits de carboneRésumé : (auteur) Throughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control). Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies. Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinel's satellite data provided by the European Copernicus program, offers new possibilities for grassland monitoring. Sentinel-1 (51) and Sentinel-2 (52) missions acquire radar and optical satellite image time series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring. In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) 52 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of 52 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging. MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused 51 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic 52 vegetation indices from both 52 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection. An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Macon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities. Numéro de notice : C2019-067 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97022 Multimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)
Titre de série : Multimodal scene understanding: algorithms, applications and deep learning, ch. 11 Titre : Decision fusion of remote-sensing data for land cover classification Type de document : Chapitre/Contribution Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Walid Ouerghemmi , Auteur ; Cyril Wendl, Auteur ; Tristan Postadjian , Auteur ; Anne Puissant, Auteur ; Clément Mallet , Auteur Editeur : Londres, New York : Academic Press Année de publication : 2019 Importance : pp 341 - 382 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] fusion de données multisource
[Termes IGN] image à très haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] occupation du sol
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (Auteur) Very high spatial resolution (VHR) multispectral imagery enables a fine delineation of objects and a possible use of texture information. Other sensors provide a lower spatial resolution but an enhanced spectral or temporal information, permitting one to consider richer land cover semantics. So as to benefit from the complementary characteristics of these multimodal sources, a decision late fusion scheme is proposed. This makes it possible to benefit from the full capacities of each sensor, while dealing with both semantic and spatial uncertainties. The different remote-sensing modalities are first classified independently. Separate class membership maps are calculated and then merged at the pixel level, using decision fusion rules. A final label map is obtained from a global regularization scheme in order to deal with spatial uncertainties while conserving the contrasts from the initial images. It relies on a probabilistic graphical model involving a fit-to-data term related to merged class membership measures and an image-based contrast-sensitive regularization term. Conflict between sources can also be integrated into this scheme. Two experimental cases are presented. In the first case one considers the fusion of VHR multispectral imagery with lower spatial resolution hyperspectral imagery for fine-grained land cover classification problem in dense urban areas. In the second case one uses SPOT 6/7 satellite imagery and Sentinel-2 time series to extract urban area footprints through a two-step process: classifications are first merged in order to detect building objects, from which a urban area prior probability is derived and eventually merged to Sentinel-2 classification output for urban footprint detection. Numéro de notice : H2019-002 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-0-12-817358-9.00017-2 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/B978-0-12-817358-9.00017-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93303
Titre : Scalable evaluation of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : 1-Pas de projet / Mallet, Clément Conférence : IGARSS 2019, IEEE International Geoscience And Remote Sensing Symposium 28/07/2019 02/08/2019 Yokohama Japon Proceedings IEEE Importance : 4 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de données
[Termes IGN] classification dirigée
[Termes IGN] fusion d'images
[Termes IGN] fusion de données
[Termes IGN] image à très haute résolution
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] taxinomieRésumé : (Auteur) The generation of 3D building models from Very High Resolution geospatial data is now an automatized procedure. However, urban areas are very complex and practitioners still have to visually assess the correctness of these models and detect reconstruction errors. We proposed an approach for automatically evaluating the quality of 3D building models. It is cast as a supervised classification task based on a hierarchical taxonomy and multimodal handcrafted features (building geometry, optical images, height data). In this paper, we evaluate how the urban area composition impacts prediction transferability and scalability of our framework to unseen scenes. This allows to define minimal feature and training sets for a problem where no benchmark data has been released so far. Numéro de notice : C2019-006 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/URBANISME Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.1109/IGARSS.2019.8899337 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1109/IGARSS.2019.8899337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92592 Semantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)
Titre : Semantic aware quality evaluation of 3D building models : Modeling and simulation Titre original : Evaluation de la qualité des modèles 3D de bâtiments Type de document : Thèse/HDR Auteurs : Oussama Ennafii , Auteur ; Clément Mallet , Directeur de thèse ; Florent Lafarge, Directeur de thèse Editeur : Champs/Marne : Université Paris-Est Année de publication : 2019 Importance : 238 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy delivered by Université Paris-Est, Speciality Geographical Information Sciences and Technologies
Thèse récompensée par le prix 2020 EuroSDR PhD Award.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'erreur
[Termes IGN] généralisation
[Termes IGN] image à très haute résolution
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] modélisation du bâti
[Termes IGN] scène urbaine
[Termes IGN] taxinomieIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The automatic generation of 3D building models from geospatial data is now a standard procedure. An abundant literature covers the last two decades and several softwares are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts, and is highly time-consuming with approximately two hours/km² per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and modular taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest or Support Vector Machine classifiers. Richer features, relying on graph kernels as well as Scattering Networks, were proposed to better take into consideration structure. Both multi-class and multi-label cases are studied: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3,000 buildings. 80%-99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It shows the necessity of multi-modal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Note de contenu : 1- Introduction
2- State of the art
3- Semantic evaluation of 3D models
4- A learning approach for quality evaluation
5- Assessing the learned approach
6- Computing a better representation
7- Assessing the advanced features
8- ConclusionNuméro de notice : 25860 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Sciences et Technologies de l'Information Géographique : Paris-Est, 2019 Organisme de stage : Lastig (IGN) nature-HAL : Thèse DOI : sans En ligne : https://hal.science/tel-02879809 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95395 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 25860-02 THESE Livre Centre de documentation Thèses Disponible 25860-01 THESE Livre Centre de documentation Thèses Disponible 25860-03 THESE Livre Centre de documentation Thèses Disponible The necessary yet complex evaluation of 3D city models: a semantic approach / Oussama Ennafii (2019)PermalinkWorkshop on Geoprocessing and Archiving of historical Aerial Images, IGN-France, Paris, June 3rd to 4th, 2019 / Clément Mallet (2019)PermalinkThe reviewing process for ISPRS events / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-5 (November 2018)PermalinkForeword to the special issue on urban remote sensing for smarter cities / Prashanth Reddy Marpu in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 11 n° 8 (August 2018)PermalinkClassification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkEditorial / Clément Mallet in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkForeword to the theme issue on geospatial computer vision / Jan Dirk Wegner in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)Permalinkvol 140 - June 2018 - Geospatial computer vision (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Jan Dirk WegnerPermalinkVers une remise en géométrie automatique des prises de vue aériennes historiques photogrammétriques / Arnaud Le Bris in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkToward automatic georeferencing of archival aerial photogrammetric surveys / Sébastien Giordano in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)Permalink
Senior researcher in LaSTIG & head of LaSTIG
HDR defense in 2016
Page perso : https://sites.google.com/view/clementmallet