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Building facade reconstruction using crowd-sourced photos and two-dimensional maps / Wu Jie in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 11 (November 2020) . - pp 677 - 694 Titre : Building facade reconstruction using crowd-sourced photos and two-dimensional maps Type de document : Article/Communication Auteurs : Wu Jie, Auteur ; Junya Mao, Auteur ; Song Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 677 - 694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] édition en libre accès [Termes descripteurs IGN] façade [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] implémentation (informatique) [Termes descripteurs IGN] reconstruction 2D du bâti [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] semis de points Résumé : (Auteur) To address the high-cost problem of the current three-dimensional (3D) reconstruction for urban buildings, a new technical framework is proposed to generate 3D building facade information using crowd-sourced photos and two-dimensional (2D) building vector data in this paper. The crowd-sourced photos mainly consisted of Tencent street view images and other-source photos, which were collected from three platforms, including search engines, social media, and mobile phones. The photos were selected and grouped first, and then a structure from motion algorithm was used for 3D reconstruction. Finally, the reconstructed point clouds were registered with 2D building vector data. The test implementation was conducted in the Jianye District of Nanjing, China, and the generated point clouds showed a good fit with the true values. The proposed 3D reconstruction method represents a multi-sourced data integration process. The advantage of the proposed approach lies in the open source and low-cost data used in this study. Numéro de notice : A2020-708 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.11.677 date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.14358/PERS.86.11.677 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96393 [article]

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Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7890 - 7907 Titre : Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting Type de document : Article/Communication Auteurs : Bufan Zhao, Auteur ; Xianghong Hua, Auteur ; Kegen Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7890 - 7907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] itération [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] regroupement de points [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points Résumé : (auteur) Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods. Numéro de notice : A2020-681 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984943 date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984943 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96205 [article]

Thermal and spatial data integration for recreating rebuilding stages of wooden and masonry buildings / Paulina Lewińska in Photogrammetric record, vol 35 n° 171 (September 2020)  

Public [article]  inPhotogrammetric record > vol 35 n° 171 (September 2020) . - pp 402-425 Titre : Thermal and spatial data integration for recreating rebuilding stages of wooden and masonry buildings Type de document : Article/Communication Auteurs : Paulina Lewińska, Auteur ; Kamil Maciuk, Auteur Année de publication : 2020 Article en page(s) : pp 402-425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] conservation du patrimoine [Termes descripteurs IGN] diffusion de données [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] image virtuelle [Termes descripteurs IGN] inventaire du bâti [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] patrimoine immobilier [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] texture d'image Résumé : (Auteur) This paper describes the means of supporting virtual reconstruction of wooden and masonry buildings with the use of thermal infrared images. Not all remaining architectural heritage can be physically preserved. War, climate change and rapidly growing cities have caused damage and degradation to valuable historical buildings. In order to preserve them virtually, various methods of 3D modelling have been employed. This paper presents information on how to create a full 3D inventory of a building’s exterior. This includes a 3D thermal model of the building, merging thermal camera data with a spatial model, which aids the reconstruction of previous stages of a building’s existence. The authors argue that in cases when it is impossible to force heat transfer from inside the structure (via man‐made heating), 24 hours of observations using only solar heating can be sufficient to find heat bridges and other thermal anomalies that can be used for virtual reconstruction. Numéro de notice : A2020-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12332 date de publication en ligne : 20/09/2020 En ligne : https://doi.org/10.1111/phor.12332 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95844 [article]

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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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)  

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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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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)  

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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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