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3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 2 (January-2 2022) . - n° 422 Titre : 3D modeling of urban area based on oblique UAS images - An end-to-end pipeline Type de document : Article/Communication Auteurs : Valeria-Ersilia Oniga, Auteur ; Ana-Ioana Breaban, Auteur ; Norbert Pfeifer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] Bâti-3D [Termes IGN] CityGML [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] image aérienne oblique [Termes IGN] image captée par drone [Termes IGN] indice de végétation [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] Roumanie [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (auteur) 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Numéro de notice : A2022-101 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14020422 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.3390/rs14020422 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99566 [article]