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An efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)  

Public 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 points Résumé : (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

Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 343 - 350 Titre : Planar polygons detection in lidar scans based on sensor topology enhanced Ransac Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Zoumana Mallé, Auteur ; Oussama Ennafii , Auteur ; Pascal Monasse, Auteur ; Bruno Vallet , Auteur Année de publication : 2020 Projets : BIOM / Vallet, Bruno Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 343 - 350 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] polygone [Termes IGN] Ransac (algorithme) [Termes IGN] segmentation en régions [Termes IGN] semis de points [Termes IGN] topologie capteur [Termes IGN] traitement de semis de points [Termes IGN] transformation de Hough Résumé : (auteur) Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation however is that these methods detect planes running across the whole scene instead of more localized planar patches. Moreover, they do not exploit the sensor information that often comes with Lidar point cloud (sensor topology and optical center position in particular). In this paper we address both issues: we aim at detecting planar polygons that have a limited spatial extent, and we exploit sensor topology. The latter is used to enhance a RANSAC framework on two aspects: to make seed points selection more local and to define more compact sets of inliers through sensor space region growing. Numéro de notice : A2020-502 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-343-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-343-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95643 [article]

A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)    

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878 Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Vallet, Bruno Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bâti-3D [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'erreur [Termes IGN] données localisées [Termes IGN] France (administrative) [Termes IGN] image à très haute résolution [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle d'erreur [Termes IGN] modèle numérique de surface [Termes IGN] qualité des données [Termes IGN] taxinomie Résumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a 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/km 2 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 versatile 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 classifier. Both multiclass and multilabel cases are considered: 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 3000 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 showed the necessity of multimodal 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. Numéro de notice : A2019-569 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 Date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440 [article]

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105-2019121	SL	Revue	Centre de documentation	Revues en salle	Disponible

Documents numériques

peut être téléchargé A learning approach to evaluate the quality of 3D city models - preprint HAL Adobe Acrobat PDF

Scalable evaluation of 3D city models / Oussama Ennafii (2019)  

Public 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 / Vallet, Bruno 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] taxinomie Résumé : (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)  

Public 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] taxinomie Index. 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- Conclusion Numéro 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

Exemplaires(3)

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)    

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Qualification sémantique de modèles 3D de bâtiments / Oussama Ennafii (2018)    

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