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Layout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (October 2021)  

Public [article]  inGeo-spatial Information Science > vol 24 n° 3 (October 2021) . - pp 403 - 421 Titre : Layout graph model for semantic façade reconstruction using laser point clouds Type de document : Article/Communication Auteurs : Hongchao Fan, Auteur ; Yuefeng Wang, Auteur ; Jianya Gong, Auteur Année de publication : 2021 Article en page(s) : pp 403 - 421 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme du recuit simulé [Termes IGN] appariement de graphes [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] enrichissement sémantique [Termes IGN] façade [Termes IGN] processus stochastique [Termes IGN] reconstruction 3D du bâti Résumé : (auteur) Building façades can feature different patterns depending on the architectural style, functionality, and size of the buildings; therefore, reconstructing these façades can be complicated. In particular, when semantic façades are reconstructed from point cloud data, uneven point density and noise make it difficult to accurately determine the façade structure. When investigating façade layouts, Gestalt principles can be applied to cluster visually similar floors and façade elements, allowing for a more intuitive interpretation of façade structures. We propose a novel model for describing façade structures, namely the layout graph model, which involves a compound graph with two structure levels. In the proposed model, similar façade elements such as windows are first grouped into clusters. A down-layout graph is then formed using this cluster as a node and by combining intra- and inter-cluster spacings as the edges. Second, a top-layout graph is formed by clustering similar floors. By extracting relevant parameters from this model, we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling. Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method. The experimental results show that the proposed method achieves an average accuracy of 86.35%. Owing to its flexibility, the proposed layout graph model can deal with different types of façades and qualities of point cloud data, enabling a more robust and accurate reconstruction of façade models. Numéro de notice : A2021-724 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10095020.2021.1922316 Date de publication en ligne : 14/05/2021 En ligne : https://doi.org/10.1080/10095020.2021.1922316 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98644 [article]

Parsing of urban facades from 3D point clouds based on a novel multi-view domain / Wei Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 283-293 Titre : Parsing of urban facades from 3D point clouds based on a novel multi-view domain Type de document : Article/Communication Auteurs : Wei Wang, Auteur ; Yuan Xu, Auteur ; Yingchao Ren, Auteur ; Gang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 283-293 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] apprentissage profond [Termes IGN] données localisées 3D [Termes IGN] façade [Termes IGN] fusion de données [Termes IGN] milieu urbain [Termes IGN] précision de la classification [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation multi-échelle [Termes IGN] semis de points Résumé : (Auteur) Recently, performance improvement in facade parsing from 3D point clouds has been brought about by designing more complex network structures, which cost huge computing resources and do not take full advantage of prior knowledge of facade structure. Instead, from the perspective of data distribution, we construct a new hierarchical mesh multi-view data domain based on the characteristics of facade objects to achieve fusion of deep-learning models and prior knowledge, thereby significantly improving segmentation accuracy. We comprehensively evaluate the current mainstream method on the RueMonge 2014 data set and demonstrate the superiority of our method. The mean intersection-over-union index on the facade-parsing task reached 76.41%, which is 2.75% higher than the current best result. In addition, through comparative experiments, the reasons for the performance improvement of the proposed method are further analyzed. Numéro de notice : A2021-333 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.283 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.283 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97531 [article]

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Implementation of close range photogrammetry using modern non-metric digital cameras for architectural documentation / Mariem A. Elhalawani in Geodesy and cartography, vol 47 n° 1 (January 2021)  

Public [article]  inGeodesy and cartography > vol 47 n° 1 (January 2021) . - pp 45 - 53 Titre : Implementation of close range photogrammetry using modern non-metric digital cameras for architectural documentation Type de document : Article/Communication Auteurs : Mariem A. Elhalawani, Auteur ; Zaki M. Zeidan, Auteur ; Ashraf A. Beshr, Auteur Année de publication : 2021 Article en page(s) : pp 45 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] caméra numérique [Termes IGN] Egypte [Termes IGN] façade [Termes IGN] longueur focale [Termes IGN] monument historique [Termes IGN] mosquée [Termes IGN] photogrammétrie architecturale [Termes IGN] point d'appui [Termes IGN] reconstruction 3D [Termes IGN] station de travail de photogrammétrie numérique Résumé : (auteur) The development of applied geodetic techniques for mapping and documentation of historical structures, buildings and sites is an important and vital purpose for contribution of any recording of cultural heritage for any country such as Egypt. This is done to preserve and restore any valuable architectural or other cultural monument, as a support to architectural, archaeological and other art-historical research throughout the ages. The purpose of this paper is to use close range photogrammetry technique (CRP) to reconstruct 3D model of architectural and historical mosque facade and comparing the accuracy of using digital commercial non-metric cameras with different resolutions and metric camera with flatbed scanner and photogrammetric scanner for architectural building documentation. El-Nasr Mosque façade in Mansoura city, Egypt was chosen as a case study in this paper. At first, twenty five points were selected at mosque façade at different elevations and distributed at different façade surfaces and observed using total station. Some of these points were selected as control points and the others were selected as check points to validate the results. Effect of control point’s number on image processing and analysis is also studied. Three cameras positions were selected for imaging to get the full details of mosque façade. Close range Digital Workstation (CDW) program was used for processing and analysis of multiple images. The results are indicated that close range photogrammetry using metric camera with photogrammetry scanner instead of flatbed scanner in technique is accurate enough to be beneficial in 3D architectural building documentation. Digital cameras with CRP technique give up different accuracy that depends mainly on the resolution of cameras and camera specifications. Numéro de notice : A2021-452 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2021.11269 Date de publication en ligne : 26/04/2021 En ligne : https://doi.org/10.3846/gac.2021.11269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97873 [article]

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 IGN] Chine [Termes IGN] données lidar [Termes IGN] données localisées des bénévoles [Termes IGN] données vectorielles [Termes IGN] édition en libre accès [Termes IGN] façade [Termes IGN] image multi sources [Termes IGN] implémentation (informatique) [Termes IGN] reconstruction 2D du bâti [Termes IGN] reconstruction 3D du bâti [Termes 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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Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 681- 707 Titre : Street-Frontage-Net: urban image classification using deep convolutional neural networks Type de document : Article/Communication Auteurs : Stephen Law, Auteur ; Chanuki Illushka Seresinhe, Auteur ; Yao Shen, Auteur Année de publication : 2020 Article en page(s) : pp 681- 707 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] espace public [Termes IGN] évaluation foncière [Termes IGN] extraction de données [Termes IGN] façade [Termes IGN] habitat urbain [Termes IGN] image Streetview [Termes IGN] immobilier (secteur) [Termes IGN] information géographique [Termes IGN] Londres [Termes IGN] matrice de confusion [Termes IGN] Paris (75) [Termes IGN] paysage urbain [Termes IGN] urbanisme [Termes IGN] vision par ordinateur Résumé : (auteur) Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design. Numéro de notice : A2020-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1555832 Date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1555832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94712 [article]

Contribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)  

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Co‐registration of panoramic mobile mapping images and oblique aerial images / Phillipp Jende in Photogrammetric record, vol 34 n° 166 (June 2019)  

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Semantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Fusion of thermal imagery with point clouds for building façade thermal attribute mapping / Dong Lin in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Developing an optimized texture mapping for photorealistic 3D buildings / Jungil Lee in Transactions in GIS, vol 23 n° 1 (February 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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Recalage conjoint de données de cartographie mobile et de modèles 3D de bâtiments / Miloud Mezian (2019)  

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Construction control and documentation of facade elements using terrestrial laser scanning / Ján Erdélyi in Applied geomatics, vol 10 n° 2 (June 2018)  

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Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information / Yongzhi Wang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 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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