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Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)  

Public [article]  inGeodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576 Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] données cadastrales [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] orthoimage couleur [Termes IGN] segmentation d'image [Termes IGN] Slovénie Résumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Point clouds for use in Building Information Models (BIM) / Robert Klinc in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)  

Public [article]  inGeodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 594 - 613 Titre : Point clouds for use in Building Information Models (BIM) Type de document : Article/Communication Auteurs : Robert Klinc, Auteur ; Uroš Jotanović, Auteur ; Klemen Kregar, Auteur Année de publication : 2021 Article en page(s) : pp 594 - 613 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canalisation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] mur [Termes IGN] qualité du modèle [Termes IGN] Revit [Termes IGN] semis de points [Termes IGN] traitement de semis de points Résumé : (Auteur) The use of point clouds in extracting data for building information modelling (BIM) has become common recently. Managers of older buildings are working to centralise information. Documentation about mechanical installations, plumbing, electricity, and previous interventions is often stored on scattered media, frequently still on paper. In the transformation of the material world into the digital world, the point cloud is the starting point, containing information about the material world obtained by various means such as photogrammetry, terrestrial or aerial laser scanning. Manual BIM modelling for management, maintenance and future use is a time-consuming and error-prone process. We would like to automate this process and avoid these errors. Recently, there have been developed an increasing number of stand-alone programmes and add-ons that provide automated, fast, and more accurate modelling based on point cloud data. In this paper, we present an investigation into the possibilities for automating the creation of BIM models from point cloud data. The result is a semi-automated process for modelling individual BIM elements, which we have tested on specific examples of modelling individual elements (walls, pipes, and columns). We note that despite the automation of the process, a high level of user interaction is still required to produce good quality models. Numéro de notice : A2021-931 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.594-613 Date de publication en ligne : 06/12/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.594-613 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99410 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021041	RAB	Revue	Centre de documentation	En réserve L003	Disponible