Détail de l'auteur
Auteur Clément Mallet
Commentaire :
Autorités liées :
idHAL :
clement-mallet
idRef :
autre URL :
ORCID :
Scopus :
Publons :
G. Scholar :
DBLP URL :
|
Documents disponibles écrits par cet auteur (186)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
An efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)
Titre : An efficient representation of 3D buildings: application to the evaluation of city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 329 - 336 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données localisées 3D
[Termes IGN] erreur systématique
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] objet géographique urbain
[Termes IGN] qualité du modèle
[Termes IGN] représentation géométrique
[Termes IGN] semis de pointsRésumé : (auteur) City modeling consists in building a semantic generalized model of the surface of urban objects. These could be seen as a special case of Boundary representation surfaces. Most modeling methods focus on 3D buildings with Very High Resolution overhead data (images and/or 3D point clouds). The literature abundantly addresses 3D mesh processing but frequently ignores the analysis of such models. This requires an efficient representation of 3D buildings. In particular, for them to be used in supervised learning tasks, such a representation should be scalable and transferable to various environments as only a few reference training instances would be available. In this paper, we propose two solutions that take into account the specificity of 3D urban models. They are based on graph kernels and Scattering Network. They are here evaluated in the challenging framework of quality evaluation of building models. The latter is formulated as a supervised multilabel classification problem, where error labels are predicted at building level. The experiments show for both feature extraction strategy strong and complementary results (F-score > 74% for most labels). Transferability of the classification is also examined in order to assess the scalability of the evaluation process yielding very encouraging scores (F-score > 86% for most labels). Numéro de notice : C2021-010 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-329-2021 Date de publication en ligne : 28/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2021-329-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98035 Assessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)
Titre : Assessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 3105 - 3108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] détection de changement
[Termes IGN] image Sentinel-SAR
[Termes IGN] parcelle agricole
[Termes IGN] prairie
[Termes IGN] régression multiple
[Termes IGN] série temporelleRésumé : (auteur) One key factor to exhaustive vegetation monitoring lies in the dense temporal sampling of the measurements. Areas subject to multiple human interventions, such as grasslands, are particularly concerned. A Recurrent Neural Network multi-sensor regression approach (SenRVM), relying on the systematic acquisitions of Sentinel-1 SAR satellite, has been thereby proposed. It permits to retrieve vegetation indexes, derived from Sentinel-2 optical imagery, despite significant cloud cover and with high sampling (6 days). The benefit of SenRVM for filling gaps in vegetation time-series describing agricultural practices is assessed. The proposed approach is compared with classical mono-sensor optical strategies. We adopt a synthetic dataset with large gaps. This realistically mimicks challenging conditions in grassland exploitation detection. Results obtained both for exploited and stable parcels satisfactorily demonstrate the relevance of our approach. Numéro de notice : C2021-042 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9554995 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9554995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99413 Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)
Titre : Combining deep learning and mathematical morphology for historical map segmentation Type de document : Chapitre/Contribution Auteurs : Yizi Chen , Auteur ; Edwin Carlinet, Auteur ; Joseph Chazalon, Auteur ; Clément Mallet , Auteur ; Bertrand Duménieu , Auteur ; Julien Perret , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2021 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12708 Projets : SODUCO / Perret, Julien Conférence : DGMM 2021, 1st International Joint Conference on Discrete Geometry and Mathematical Morphology 24/05/2021 27/05/2021 Uppsala Suède Proceedings Springer Importance : pp 79 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage profond
[Termes IGN] carte ancienne
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données maillées
[Termes IGN] morphologie mathématique
[Termes IGN] vectorisationRésumé : (auteur) The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps. Numéro de notice : H2021-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1007/978-3-030-76657-3_5 Date de publication en ligne : 16/05/2021 En ligne : https://hal.science/hal-03101578v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96739
Titre : Contextual land-cover map translation with semantic segmentation Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : MAESTRIA / Mallet, Clément Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 2488 - 2491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] base de données d'occupation du sol
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corine Land Cover
[Termes IGN] France (administrative)
[Termes IGN] segmentation sémantique
[Termes IGN] transformation géométrique
[Termes IGN] translationRésumé : (auteur) This paper presents a framework for translating a land-cover map into another one in a supervised way. This links to numerous applications (updating, completion, etc.). Conversely to existing approaches, we jointly perform spatial and semantic transformation without any prior knowledge. The proposed method assumes that: i) examples of the source and target maps already exist, ii) the spatial resolution of the source map is equal or higher than the target one. The translation is performed using an asymmetric Convolutional Neural Network with positional encoding. Experimental results show the effectiveness of the method in retrieving a yearly version of Corine Land Cover (CLC) at country-scale (France) using an existing high-resolution map and with similar accuracy than existing CLC maps (~80%). Numéro de notice : C2021-049 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9553693 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9553693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99414
Titre : ICDAR 2021 competition on historical map segmentation Type de document : Article/Communication Auteurs : Joseph Chazalon, Auteur ; Edwin Carlinet, Auteur ; Yizi Chen , Auteur ; Julien Perret , Auteur ; Bertrand Duménieu , Auteur ; Clément Mallet , Auteur ; Thierry Géraud, Auteur ; Vincent Nguyen, Auteur ; Nam Nguyen, Auteur ; Josef Baloun, Auteur ; Ladislav Lenc, Auteur ; Pavel Král, Auteur Editeur : Le Kremlin Bicêtre : Ecole pour l'Informatique et les Techniques Avancées EPITA Année de publication : 2021 Projets : 1-Pas de projet / Mallet, Clément Conférence : ICDAR 2021, 16th International Conference on Document Analysis and Recognition 05/09/2021 10/09/2021 Lausanne Suisse Proceedings IEEE Importance : 15 p. Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task 2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task 3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks 2 and 3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at https://icdar21-mapseg.github.io/. Numéro de notice : C2021-022 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-03256193 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98032 PermalinkLearning to translate land-cover maps: Several multi-dimensional context-wise solutions / Luc Baudoux (2021)PermalinkVectorization of historical maps using deep edge filtering and closed shape extraction / Yizi Chen (2021)PermalinkVegetation stratum occupancy prediction from airborne LiDAR 3D point clouds / Ekaterina Kalinicheva (2021)Permalinkvol V-4-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 4, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkvol V-5-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 5, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkSemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)Permalinkvol V-1-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 1, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkvol V-2-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 2, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkvol V-3-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 3, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas PaparoditisPermalinkCNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkPreface: the 2020 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2020 (August 2020)PermalinkClassification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)PermalinkPermalinkForeword to the special issue on paving the way for the future of urban remote sensing / Sébastien Lefèvre in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 13 ([01/01/2020])PermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkUso de QGIS en la teledetección, Vol. 1. QGIS y las herramientas genéricas / Nicolas Baghdadi (2020)PermalinkUso de QGIS en la teledetección, Vol. 2. QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)PermalinkUso de QGIS en la teledetección, Vol. 3. QGIS y aplicaciones en la ordenación del territorio / Nicolas Baghdadi (2020)PermalinkUso de QGIS en la teledetección, Vol. 4. QGIS y sus aplicaciones en agua y en gestion del riego / Nicolas Baghdadi (2020)Permalink
Senior researcher in LaSTIG & head of LaSTIG
HDR defense in 2016
Page perso : https://sites.google.com/view/clementmallet