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Development and application of an intelligent modeling method for ancient wooden architecture / Yonghui Jiang in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)

Public [article]  inISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 19 p. Titre : Development and application of an intelligent modeling method for ancient wooden architecture Type de document : Article/Communication Auteurs : Yonghui Jiang, Auteur ; Aiqun Li, Auteur ; Linlin Xie, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Architecture [Termes descripteurs IGN] architecture orientée modèle [Termes descripteurs IGN] Chine [Termes descripteurs IGN] conservation du patrimoine [Termes descripteurs IGN] histoire [Termes descripteurs IGN] modélisation 3D du bâti BIM [Termes descripteurs IGN] monument historique [Termes descripteurs IGN] patrimoine culturel [Termes descripteurs IGN] patrimoine immobilier [Termes descripteurs IGN] reconstruction 3D du bâti Résumé : (auteur) Building-information-modeling for cultural heritage (HBIM), which is established using surveying data, can be used to conserve architectural heritage. The development of an HBIM model for ancient wooden architecture (AWA) structures requires interdisciplinary integration. A parametric model for the main components that intelligently integrates the historical knowledge, as well as an intelligent modeling method for these components, are two critical issues required to bridge the existing gap and improve the application of HBIM. Taking an AWA structure constructed during the Liao and Song Dynasties as an example, the parametric model for the typical components, with emphasis on commonality and characteristics, were first proposed. Subsequently, an intelligent automated modeling method was developed and programmed using Dynamo, which can intelligently identify the component type and determine the invisible dimensions. A complicated dou-gong was successfully established with surveying data using the proposed method within five minutes, thereby validating the reliability and efficiency of this method. Furthermore, the proposed method was used to establish the HBIM model of Yingxian Wood Pagoda, which is the oldest and tallest AWA structure in China with a height of 65.88 m. The research findings will provide an essential reference for the conservation of wooden architectural heritage structures. Numéro de notice : A2020-258 Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95015 [article]

Remote sensing based building extraction / Mohammad Awrangjeb (2020)  

Public contenu dans Directory of Open Access Books DOAB / Directory of Open Access Books DOAB   Titre : Remote sensing based building extraction Type de document : Monographie Auteurs : Mohammad Awrangjeb, Auteur ; Xiangyun Hu, Auteur ; Bisheng Yang, Auteur ; Jiaojiao Tian, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 442 p. ISBN/ISSN/EAN : 978-3-03928-383-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Editeur) Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D. Numéro de notice : 26305 Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-383-5 date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/books978-3-03928-383-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95064

Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8600 - 8617 Titre : Introducing spatial regularization in SAR tomography reconstruction Type de document : Article/Communication Auteurs : Clément Rambour, Auteur ; Loïc Denis, Auteur ; Florence Tupin, Auteur ; Hélène Oriot, Auteur Année de publication : 2019 Article en page(s) : pp 8600 - 8617 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] écho radar [Termes descripteurs IGN] fractionnement [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] mécanique de Lagrange [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] TerraSAR-X [Termes descripteurs IGN] tomographie radar Résumé : (auteur) The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces. Numéro de notice : A2019-596 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921756 date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94588 [article]

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

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 435 - 443 Titre : RoofN3D: a database for 3D building reconstruction with deep learning Type de document : Article/Communication Auteurs : Andreas Wichmann, Auteur ; Amgad Agoub, Auteur ; Valentina Schmidt, Auteur Année de publication : 2019 Article en page(s) : pp 435 - 443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] .Net [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données localisées 3D [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] toit Résumé : (Auteur) Machine learning methods, in particular those based on deep learning, have gained in importance through the latest development of artificial intelligence and computer hardware. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a three-dimensional (3D) point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. The details about RoofN3D and the developed framework to automatically derive such training data are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to 3D point cloud data are presented. Finally, we exemplarily demonstrate how the provided data can be used to classify building roofs with the PointNet framework. Numéro de notice : A2019-248 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.435 En ligne : https://doi.org/10.14358/PERS.85.6.435 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93004 [article]

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Automatic reconstruction of fully volumetric 3D building models from oriented point clouds / Sebastian Ochmann in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 251 - 262 Titre : Automatic reconstruction of fully volumetric 3D building models from oriented point clouds Type de document : Article/Communication Auteurs : Sebastian Ochmann, Auteur ; Richard Vock, Auteur ; Reinhard Klein, Auteur Année de publication : 2019 Article en page(s) : pp 251 - 262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Industry foudation classes IFC [Termes descripteurs IGN] modélisation 3D du bâti BIM [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] programmation linéaire [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] semis de points Résumé : (Auteur) We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds. Numéro de notice : A2019-210 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.017 date de publication en ligne : 30/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92676 [article]

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Geometric comparison and quality evaluation of 3D models of indoor environments / H. Tran in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Repeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)  

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

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Digital preservation, social history, and the Quon Sang Lung Laundry building : a case study from Fort Macleod, Alberta, Canada / Peter Dawson in Applied geomatics, vol 10 n° 4 (December 2018)  

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Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (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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On a novel 360° panoramic stereo mobile mapping system / Stefan Blaser in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)  

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

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Documentation of heritage buildings using close-range UAV images: dense matching issues, comparison and case studies / Arnadi Murtiyoso in Photogrammetric record, vol 32 n° 159 (September 2017)  

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Urban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)  

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