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Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 17 - 28 Titre : Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Li Li, Auteur ; Nan Song, Auteur ; Fei Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation 3D [Termes IGN] Perceptron multicouche [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) Three-dimensional (3D) building roof reconstruction from airborne LiDAR point clouds is an important task in photogrammetry and computer vision. To automatically reconstruct the 3D building models at Level of Detail 2 (LoD-2) from airborne LiDAR point clouds, the data-driven approaches usually need to be performed in two steps: geometric primitive extraction and roof structure inference. Obviously, the traditional approaches are not end-to-end, the accumulated errors in different stages cannot be avoided and the final 3D roof models may not be optimal. In addition, the results of 3D roof models largely depend on the accuracy of geometric primitives (planes, lines, etc.). To solve these problems, we present a deep learning-based approach to directly reconstruct building roofs from airborne LiDAR point clouds, named Point2Roof. In our method, we start by extracting the deep features for each input point using PointNet++. Then, we identify a set of candidate corner points from the input point clouds using the extracted deep features. In addition, we also regress the offset for each candidate corner point to refine their locations. After that, these candidates are clustered into a set of initial vertices, and we further refine their locations to obtain the final accurate vertices. Finally, we propose a Paired Point Attention (PPA) module to predict the true model edges from an exhaustive set of candidate edges between the vertices. Unlike traditional roof modeling approaches, the proposed Point2Roof is end-to-end. However, due to the lack of a building reconstruction dataset, we construct a large-scale synthetic dataset to verify the effectiveness and robustness of the proposed Point2Roof. The experimental results conducted on the synthetic benchmark demonstrate that the proposed Point2Roof significantly outperforms the traditional roof modeling approaches. The experiments also show that the network trained on the synthetic dataset can be applied to the real point clouds after fine-tuning the trained model on a small real dataset. The large-scale synthetic dataset, the small real dataset and the source code of our approach are publicly available in https://github.com/Li-Li-Whu/Point2Roof. Numéro de notice : A2022-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.027 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101728 [article]

An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 179 (September 2022) . - pp 260 - 284 Titre : An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur Année de publication : 2022 Article en page(s) : pp 260 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] analyse de groupement [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] fusion de données [Termes IGN] Norvège [Termes IGN] Ransac (algorithme) [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data-sets or they are not automatic and efficient. To tackle these issues, an improved multi-task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean-shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data-set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data-set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task. Numéro de notice : A2022-936 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12420 Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1111/phor.12420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102682 [article]

City3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2254 Titre : City3D: Large-scale building reconstruction from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Jin Huang, Auteur ; Jantien E. Stoter, Auteur ; Ravi Peters, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] empreinte [Termes IGN] mur [Termes IGN] polygonale [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit [Termes IGN] Triangular Regular Network [Termes IGN] triangulation de Delaunay Résumé : (auteur) We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications. Numéro de notice : A2022-387 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14092254 Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.3390/rs14092254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100667 [article]

Automated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 165 - 170 Titre : Automated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands Type de document : Article/Communication Auteurs : Ravi Peters, Auteur ; Balazs Dukai, Auteur ; Stelios Vitalis, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 165 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] itération [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] niveau de détail [Termes IGN] Pays-Bas [Termes IGN] qualité des données [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit [Termes IGN] Web Map Tile Service Résumé : (auteur) In this paper, we present our workflow to automatically reconstruct three-dimensional (3D) building models based on two-dimensional building polygons and a lidar point cloud. The workflow generates models at different levels of detail (LoDs) to support data requirements of different applications from one consistent source. Specific attention has been paid to make the workflow robust to quickly run a new iteration in case of improvements in an algorithm or in case new input data become available. The quality of the reconstructed data highly depends on the quality of the input data and is monitored in several steps of the process. A 3D viewer has been developed to view and download the openly available 3D data at different LoDs in different formats. The workflow has been applied to all 10 million buildings of the Netherlands. The 3D service will be updated after new input data becomes available. Numéro de notice : A2022-200 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00032R2 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.21-00032R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100002 [article]

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Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)

Public [article]  inGéomatique expert > n° 136 (novembre - décembre 2021) . - pp 42 -47 Titre : Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 42 -47 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage profond [Termes IGN] CityGML [Termes IGN] classification automatique d'objets [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] emprise au sol [Termes IGN] maquette numérique [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique du bâti [Termes IGN] modélisation 3D [Termes IGN] niveau de détail [Termes IGN] orthoimage [Termes IGN] primitive géométrique [Termes IGN] toit Résumé : (Auteur) Post-traitement des données issues de l’algorithme d’apprentissage profond et modélisation 3D urbaine automatique Numéro de notice : A2021-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/11/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99335 [article]

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A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

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Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud / Marlo Antonio Ribeiro Martins in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

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Automatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards / Erwann Le Roux in Natural Hazards and Earth System Sciences, vol 20 n° 11 (November 2020)  

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Outlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)  

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Modelling of buildings from aerial LiDAR point clouds using TINs and label maps / Minglei Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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RoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Piecewise horizontal 3D roof reconstruction from aerial Lidar / Slim Namouchi (2019)  

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Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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A greyscale voxel model for airborne lidar data applied to building detection / Liying Wang in Photogrammetric record, vol 33 n° 164 (December 2018)  

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Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)  

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Three-dimensional building façade segmentation and opening area detection from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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Geometric reasoning with uncertain polygonal faces / Jochen Meidow in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)  

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Use of LiDAR for calculating solar irradiance on roofs and façades of buildings at city scale: Methodology, validation, and analysis / Liang Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Building extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm / Saeid Parsian in Geomatica [en ligne], vol 71 n° 4 (December 2017)  

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3d roof model generation and analysis supporting solar system positioning / Filiberto Chiabrando in Geomatica [en ligne], vol 71 n° 3 (September 2017)  

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3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database / Rujun Cao in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

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Hierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)  

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Automatic detection and reconstruction of 2-D/3-D building shapes from spaceborne TomoSAR point clouds / Muhammad Shahzad in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

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Building cities using UAV / Martin Schwall in Geoinformatics, vol 18 n° 8 (December 2015)

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Full-waveform data for building roof step edge localization / Małgorzata Słota in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)  

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Restitutions de toitures à partir de nuages de points LiDAR / Thomas Lüthi in Géomatique suisse, vol 113 n° 8 (août 2015)  

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A fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

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Flexible building primitives for 3D building modeling / B. Xiong in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)  

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A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds / B. Xiong in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)  

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Segmentation d'images aériennes par coopération LPE-régions et LPE-contours, application à la caractérisation de toitures / Youssef El Merabet in Revue Française de Photogrammétrie et de Télédétection, n° 206 (Avril 2014)  

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Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification / Markus Gerke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)  

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Recording complex structures using close range photogrammetry: The cathedral of Santiago De Compostela / Santiago Martinez in Photogrammetric record, vol 28 n° 144 (December 2013 - February 2014)  

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Ableitung von 3D-Gebäudeobjekten aus 3D-Flächen und Katastergrundissen mit Hilfe von SketchUp© / Tobias Jung in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, vol 138 n° 5 (01/09/2013)

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Automatic extraction of building roofs using LIDAR data and multispectral imagery / Mohammad Awrangjeb in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)  

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A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data / Hai Huang in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)  

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Model driven reconstruction of roofs from sparse LIDAR point clouds / A. Henn in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)  

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Automated planimetric quality control in high accuracy airborne laser scanning surveys / M. George Vosselman in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)  

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Detection of areas for rainwater harvesting using airborne laser scanner and aerial imagery / Jorge Antonio Silva Centeno in Revue Française de Photogrammétrie et de Télédétection, n° 198 - 199 (Septembre 2012)  

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Lidar strip adjustment with automatically reconstructed roof shapes / M. Rentsch in Photogrammetric record, vol 27 n° 139 (September - November 2012)  

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A multi-resolution hybrid approach for building model reconstruction from lidar data / M. Satari in Photogrammetric record, vol 27 n° 139 (September - November 2012)  

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Photogrammetric control points from airborne laser scanner data / Q. Dalmolin in Revue Française de Photogrammétrie et de Télédétection, n° 198 - 199 (Septembre 2012)  

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Cadastre solaire : recherche systématique des toits les plus appropriés pour les installations photovoltaïques / D. Klauser in Géomatique suisse, vol 110 n° 5 (01/05/2012)

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Extraction of building roof contours from LiDAR data using a Markov-random-field-based approach / E. Dos Santos Galvanin in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)  

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Modelling the Zn emissions from roofing materials at Créteil city scale - Defining a methodology / Emna Sellami-Kaaniche (2012)  

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Photogrammétrie et photographies : des outils complémentaires pour la reconstitution numérique du patrimoine. L'exemple de la modélisation des toits de deux bâtiments de terre de la ville de Bam (Iran) / Olivier Bouet in Revue Française de Photogrammétrie et de Télédétection, n° 196 (Janvier 2012)  

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Automatic roof model reconstruction from ALS data and 2D ground plans based on side projection and the TMR [TIN-Merging and Reshaping] algorithm / J. Rau in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

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Surveying and modelling the Main Spire of Milan Cathedral using multiple data sources / F. Fassi in Photogrammetric record, vol 26 n° 136 (December 2011 - February 2012)  

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POTSOL : analyse du potentiel photovoltaïque des toits de bâtiments / F. Grin in Géomatique suisse, vol 109 n° 11 (01/11/2011)

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A quality prediction method for building model reconstruction using LiDAR data and topographic maps / R. You in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

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Building roof modeling from airborne laser scanning data based on level set approach / Kamyoung Kim in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)  

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Web-based solar roof cadastre goes international / S. Lanig in GEO: Geoconnexion international, vol 10 n° 7 (July – august 2011)

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Contributions to the 3D city modeling. 3D polyhedral building model reconstruction from aerial images & 3D facade modeling from terrestrial 3D point cloud and images / Karim Hammoudi (2011)  

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Interpretation of 2D and 3D building details on facade and roofs / Philip Meixner (2011)    

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Generation of complex polyhedral building models by integrating stereo-aerial imagery and Lidar data / A. Habib in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 5 (May 2010)  

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Home energy assessment technologies / G. Hay in GIM international, vol 24 n° 3 (March 2010)

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Segmentation and reconstruction of polyhedral building roofs from aerial lidar points clouds / A. Sampath in IEEE Transactions on geoscience and remote sensing, vol 48 n° 3 Tome 2 (March 2010)  

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Change detection in submetric optical images using land cover classification tools / Arnaud Le Bris (2010)

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Modélisation 3D de bâtiments / Mathieu Brédif (2010)  

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3D builbing reconstruction from lidar based on a cell decomposition approach / Martin Kada (01/12/2009)  

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Automated extraction of buildings from Ikonos imagery by integrating spectral and spatial information / X. Wang in Geomatica, vol 63 n° 3 (September 2009)

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Classification of roof materials for rainwater pollution modelization / Arnaud Le Bris (2009)    

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Topographic laser ranging and scanning, ch 14. Feature extraction from lidar data in urban areas / Frédéric Bretar (2009)

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A Polygonal approach for automation in extraction of serial modular roofs / Y. Avrahami in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

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Using a binary space partitioning tree for reconstructing polyhedral building models from airborne Lidar data / Gunho Sohn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

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Videogrammetric monitoring of as-built membrane roof structures / S.Y. Lin in Photogrammetric record, vol 23 n° 122 (June - August 2008)  

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Photogrammetric and LIDAR data integration using the centroid of a rectangular roof as a control point / Edson Aparecido Mitishita in Photogrammetric record, vol 23 n° 121 (March - May 2008)  

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An efficient approach to building superstructure reconstruction using digital elevation maps / Fadi Dornaika (2008)  

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A stochastic framework for the identification of building rooftops using a single remote sensing image / A. Katartzis in IEEE Transactions on geoscience and remote sensing, vol 46 n° 1 (January 2008)  

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Construction et intégration de maquettes 3D dans un SIG / M. Koehl in Géomatique expert, n° 58 (01/09/2007)

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Semi-automatic approach toward mapping of flat-roofed buildings within a non-stereoscopic environment / Y. Avrahami in Photogrammetric record, vol 22 n° 117 (March - May 2007)  

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3D building reconstruction with parametric roof superstructures / Mathieu Brédif (2007)  

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Détection et reconstruction de facettes 3D par approche hiérarchique par régions, à partir de couples d'images satellite THR / Nesrine Chehata in Bulletin d'information scientifique et technique de l'IGN, n° 75 (mars 2006)

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Automatic building reconstruction from a digital elevation model and cadastral data : an operational approach / Mélanie Durupt (2006)    

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A supervised classification approach towards quality self-diagnosis of 3D building models using digital aerial imagery / Laurence Boudet (2006)    

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