{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:20:34Z","timestamp":1776374434021,"version":"3.51.2"},"reference-count":102,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (\u201ctexture-based\u201d approach) or directly on the 3D data (\u201cgeometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological\/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored.<\/jats:p>","DOI":"10.3390\/rs11070847","type":"journal-article","created":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T11:54:52Z","timestamp":1554724492000},"page":"847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Classification of 3D Digital Heritage"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3400-9364","authenticated-orcid":false,"given":"Eleonora","family":"Grilli","sequence":"first","affiliation":[{"name":"3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38121 Trento, Italy"},{"name":"Department of Architecture, Alma Mater Studiorum\u2014University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-5342","authenticated-orcid":false,"given":"Fabio","family":"Remondino","sequence":"additional","affiliation":[{"name":"3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38121 Trento, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1260\/2047-4970.2.4.695","article-title":"Management of architectural heritage information in BIM and GIS: State-of-the-art and future perspectives","volume":"2","author":"Saygi","year":"2013","journal-title":"Int. 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