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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]

VGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images / Chaoquan Zhang in Journal of Geovisualization and Spatial Analysis, vol 5 n° 2 (December 2021)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 5 n° 2 (December 2021) . - n° 18 Titre : VGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur ; Gefei Kong, Auteur Année de publication : 2021 Article en page(s) : n° 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse de sensibilité [Termes IGN] approche participative [Termes IGN] base de données relationnelles [Termes IGN] CityGML [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] information sémantique [Termes IGN] interactivité [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] reconstruction 3D du bâti Résumé : (auteur) Applications in smart cities are inseparable from the usage of three-dimensional (3D) building models. However, the cost of generating and constructing 3D building models with semantic information is high both in time and in labour. To solve this problem, we developed a web-based interactive system, VGI3D, with the ambition of becoming a VGI platform to collect 3D building models with semantic information by using the power of crowdsourcing. VGI3D is a platform-independent software program that is composed of a spatially relational database (PostgreSQL/PostGIS) for the storage and management of spatially geometrical data and other software modules, allowing users to import, analyse, reconstruct, visualise, modify and export 3D building models according to the OBJ/CityGML standard. In this paper, we present the VGI3D in detail, focusing on relevant technical implementations, and report the results of limited usability testing aimed at optimising the system and user experience. After limited expert and non-expert participants’ testing, we proved the usefulness of VGI3D and its promising value for the 3D modelling community. Numéro de notice : A2021-884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-021-00086-7 Date de publication en ligne : 23/09/2021 En ligne : https://doi.org/10.1007/s41651-021-00086-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99205 [article]