{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T01:29:19Z","timestamp":1783733359672,"version":"3.55.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031174384","type":"print"},{"value":"9783031174391","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17439-1_24","type":"book-chapter","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T07:05:19Z","timestamp":1665126319000},"page":"329-341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Comprehensive Understanding of Machine Learning and Deep Learning Methods for 3D Architectural Cultural Heritage Point Cloud Semantic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1803-1406","authenticated-orcid":false,"given":"Yuwei","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9469-4159","authenticated-orcid":false,"given":"Simone","family":"Teruggi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6555-8310","authenticated-orcid":false,"given":"Francesco","family":"Fassi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4058-6176","authenticated-orcid":false,"given":"Marco","family":"Scaioni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.autcon.2017.09.023","volume":"85","author":"LJ S\u00e1nchez-Aparicio","year":"2018","unstructured":"S\u00e1nchez-Aparicio, L.J., Del Pozo, S., Ramos, L.F., Arce, A., Fernandes, F.: Heritage site preservation with combined radiometric and geometric analysis of TLS data. Autom. Constr. 85, 24\u201339 (2018). https:\/\/doi.org\/10.1016\/j.autcon.2017.09.023","journal-title":"Autom. Constr."},{"issue":"1","key":"24_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.aei.2009.08.006","volume":"24","author":"F Bosch\u00e9","year":"2010","unstructured":"Bosch\u00e9, F.: Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv. Eng. Inform. 24(1), 107\u2013118 (2010). https:\/\/doi.org\/10.1016\/j.aei.2009.08.006","journal-title":"Adv. Eng. Inform."},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103131","volume":"113","author":"T Czerniawski","year":"2020","unstructured":"Czerniawski, T., Leite, F.: Automated digital modeling of existing buildings: a review of visual object recognition methods. Autom. Constr. 113, 103131 (2020). https:\/\/doi.org\/10.1016\/j.autcon.2020.103131","journal-title":"Autom. Constr."},{"key":"24_CR4","doi-asserted-by":"publisher","first-page":"B4014009","DOI":"10.1061\/(ASCE)CP.1943-5487.0000406","volume":"29","author":"Y Ham","year":"2015","unstructured":"Ham, Y., Golparvar-Fard, M.: Three-dimensional thermography-based method for cost-benefit analysis of energy efficiency building envelope retrofits. J. Comput. Civ. Eng. 29, B4014009 (2015). https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000406","journal-title":"J. Comput. Civ. Eng."},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Teruggi, S., Grilli, E., Russo, M., Fassi, F., Remondino, F.: A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification. Remote Sens. 12(16), 2598 (2020). https:\/\/doi.org\/10.3390\/rs12162598","DOI":"10.3390\/rs12162598"},{"key":"24_CR6","doi-asserted-by":"publisher","unstructured":"Weinmann, M., Jutzi, B., Mallet, C., Weinmann, M.: Geometric features and their relevance for 3D point cloud classification. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.,\u00a0 IV-1\/W1, 157\u2013164 (2017).https:\/\/doi.org\/10.5194\/isprs-annals-IV-1-W1-157-2017","DOI":"10.5194\/isprs-annals-IV-1-W1-157-2017"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Grilli, E., Farella, E. M., Torresani, A., Remondino, F.: Geometric features analysis for the classification of cultural heritage point clouds. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.\u00a0 XLII-2\/W15, 541\u2013548 (2019). https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-W15-541-2019","DOI":"10.5194\/isprs-archives-XLII-2-W15-541-2019"},{"key":"24_CR8","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3390\/ijgi9060379","volume":"9","author":"E Grilli","year":"2020","unstructured":"Grilli, E., Remondino, F.: Machine learning generalization across different 3D architectural heritage. ISPRS Int. J. Geo-Inf. 9, 379 (2020). https:\/\/doi.org\/10.3390\/ijgi9060379","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. Tog. 38, 1\u201312 (2019). https:\/\/doi.org\/10.1145\/3326362","journal-title":"ACM Trans. Graph. Tog."},{"key":"24_CR10","doi-asserted-by":"publisher","unstructured":"Matrone, F., et al.: A benchmark for large-scale heritage point cloud semantic segmentation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B2\u20132020,\u00a0 1419\u20131426 (2020). https:\/\/doi.org\/10.5194\/isprs-archives-XLIII-B2-2020-1419-2020","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1419-2020"},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Tommasi, C., Fiorillo, F., Jim\u00e9nez Fern\u00e1ndez-Palacios, B., Achille, C.: Access and web-sharing of 3D digital documentation of environmental and architectural heritage. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-2\/W9, 707\u2013714 (2019). https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-W9-707-2019","DOI":"10.5194\/isprs-archives-XLII-2-W9-707-2019"},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Mathias, M., Martinovic, A., Weissenberg, J., Haegler, S., Van Gool, L.: Automatic architectural style recognition. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,\u00a0 XXXVIII-5-W16, 171\u2013176 (2011). https:\/\/doi.org\/10.5194\/isprsarchivesXXXVIII-5-W16-171-2011","DOI":"10.5194\/isprsarchivesXXXVIII-5-W16-171-2011"},{"key":"24_CR13","doi-asserted-by":"publisher","unstructured":"Ho, T.K.: Random decision forests. In: 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278\u2013282. IEEE, Montreal, QC, Canada (1995).\u00a0\u00a0https:\/\/doi.org\/10.1109\/ICDAR.1995.598994","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"24_CR15","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.3390\/rs11121499","volume":"11","author":"D Griffiths","year":"2019","unstructured":"Griffiths, D., Boehm, J.: A review on deep learning techniques for 3D sensed data classification. Remote Sens. 11, 1499 (2019). https:\/\/doi.org\/10.3390\/rs11121499","journal-title":"Remote Sens."},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Grilli, E., Dininno, D., Petrucci, G., Remondino, F.: From 2D to 3D supervised segmentation and classification for cultural heritage applications.\u00a0 Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.\u00a0 XLII-2, 399\u2013406 (2018). https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-399-2018","DOI":"10.5194\/isprs-archives-XLII-2-399-2018"},{"key":"24_CR17","doi-asserted-by":"publisher","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep learning on point sets for 3D classification and segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660. IEEE, Honolulu, HI, USA (2017).\u00a0https:\/\/doi.org\/10.48550\/arXiv.1612.00593","DOI":"10.48550\/arXiv.1612.00593"},{"key":"24_CR18","doi-asserted-by":"publisher","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: Flexible and deformable convolution for point clouds. In: IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420. IEEE, Seoul, South Korea (2019). https:\/\/doi.org\/10.48550\/arXiv.1904.08889","DOI":"10.48550\/arXiv.1904.08889"},{"key":"24_CR19","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/rs12061005","volume":"12","author":"R Pierdicca","year":"2020","unstructured":"Pierdicca, R., et al.: Point cloud semantic segmentation using a deep learning framework for cultural heritage. Remote Sens. 12, 1005 (2020). https:\/\/doi.org\/10.3390\/rs12061005","journal-title":"Remote Sens."},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3390\/ijgi9090535","volume":"9","author":"F Matrone","year":"2020","unstructured":"Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., Remondino, F.: Comparing machine and deep learning methods for large 3D heritage semantic segmentation. ISPRS Int. J. Geo-Inf. 9, 535 (2020). https:\/\/doi.org\/10.3390\/ijgi9090535","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"24_CR21","doi-asserted-by":"publisher","first-page":"8996","DOI":"10.3390\/app11198996","volume":"11","author":"Y Cao","year":"2021","unstructured":"Cao, Y., Scaioni, M.: 3DLEB-Net: Label-efficient deep learning-based semantic segmentation of building point clouds at LoD3 level. Appl. Sci. 11, 8996 (2021). https:\/\/doi.org\/10.3390\/app11198996","journal-title":"Appl. Sci."},{"issue":"4","key":"24_CR22","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/s12518-018-0225-3","volume":"10","author":"C Achille","year":"2018","unstructured":"Achille, C., Fassi, F., Mandelli, A., Fiorillo, F.: Surveying cultural heritage: summer school for conservation activities. Appl. Geomatics 10(4), 579\u2013592 (2018). https:\/\/doi.org\/10.1007\/s12518-018-0225-3","journal-title":"Appl. Geomatics"},{"key":"24_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-540-31865-1_25","volume-title":"Advances in Information Retrieval","author":"C Goutte","year":"2005","unstructured":"Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fern\u00e1ndez-Luna, J.M. (eds.) Advances in Information Retrieval. Lecture Notes in Computer Science, vol. 3408, pp. 345\u2013359. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31865-1_25"}],"container-title":["Communications in Computer and Information Science","Geomatics for Green and Digital Transition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17439-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T16:23:49Z","timestamp":1676046229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17439-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031174384","9783031174391"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17439-1_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASITA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italian Conference on Geomatics and Geospatial Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Genova","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asita2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.asita.it\/en\/asita-2022-conference\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}