{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T14:11:34Z","timestamp":1775225494478,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015564","name":"Universit\u00e0 Italo Francese","doi-asserted-by":"publisher","award":["VINCI2019 - Capitolo II, Contributi di mobilit\u00e0 per tesi di dottorato in co-tutela"],"award-info":[{"award-number":["VINCI2019 - Capitolo II, Contributi di mobilit\u00e0 per tesi di dottorato in co-tutela"]}],"id":[{"id":"10.13039\/100015564","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The \u201cGrand-Ducal Cloister\u201d dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed.<\/jats:p>","DOI":"10.3390\/rs13030461","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T09:03:45Z","timestamp":1611824625000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9601-3145","authenticated-orcid":false,"given":"Valeria","family":"Croce","sequence":"first","affiliation":[{"name":"Department of Civil and Industrial Engineering, ASTRO Laboratory, University of Pisa, 56122 Pisa, Italy"},{"name":"Mod\u00e8les et Simulations pour l\u2019Architecture et le Patrimoine, UMR 3495 CNRS\/MC, 13402 Marseille, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3065-0616","authenticated-orcid":false,"given":"Gabriella","family":"Caroti","sequence":"additional","affiliation":[{"name":"Department of Civil and Industrial Engineering, ASTRO Laboratory, University of Pisa, 56122 Pisa, Italy"}]},{"given":"Livio","family":"De Luca","sequence":"additional","affiliation":[{"name":"Mod\u00e8les et Simulations pour l\u2019Architecture et le Patrimoine, UMR 3495 CNRS\/MC, 13402 Marseille, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-0165","authenticated-orcid":false,"given":"K\u00e9vin","family":"Jacquot","sequence":"additional","affiliation":[{"name":"MAP-ARIA, UMR 3495 CNRS\/MC, ENSA Lyon, 69120 Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0582-5314","authenticated-orcid":false,"given":"Andrea","family":"Piemonte","sequence":"additional","affiliation":[{"name":"Department of Civil and Industrial Engineering, ASTRO Laboratory, University of Pisa, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4062-2432","authenticated-orcid":false,"given":"Philippe","family":"V\u00e9ron","sequence":"additional","affiliation":[{"name":"LISPEN EA 7515, Arts et M\u00e9tiers ParisTech, 13617 Aix-en-Provence, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5194\/isprs-archives-XLII-2-W11-121-2019","article-title":"HBIM Methodologies for the Architectural Restoration. 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