A Novel Method for Detecting and Quantifying Damages in Masonry Architectural Heritage Based on Deep Learning of Point Cloud Data

A Novel Method for Detecting and Quantifying Damages in Masonry Architectural Heritage Based on Deep Learning of Point Cloud Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Method for Detecting and Quantifying Damages in Masonry Architectural Heritage Based on Deep Learning of Point Cloud Data Fan Sun, Qing Chun, Shiyu Ma, Yu Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9620909/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract With the development of deep learning, the surface damage detection of masonry architectural heritage in 2D images has achieved remarkable progress. However, 2D images damage detection still faces challenges such as blind areas, shadows, and a lack of depth information in detection. The multi-perspective point cloud can address these issues by reconstructing the obscured area. Meanwhile, point clouds provide depth information, enabling the quantification of damage geometry. In order to effectively identify and quantify the damages on the surface of masonry architectural heritage, a method based on deep learning of point clouds is proposed, including point cloud preprocessing, automatic identification using an improved PointNet++ model and automatic quantification by the morphological analysis. The results demonstrated that, in terms of damage identification, the improved PointNet++ model with optimized parameters achieves high performance in damage identification, with an accuracy of 94.2% and an MIoU of 84.5%. In terms of damage quantification, the automated quantification could effectively capture the geometric damage information, with errors in area and depth measurements both below 10%. These findings indicate that the proposed method can identify and quantify damages with high accuracy, providing a scientific and effective approach for masonry buildings inspection. deep learning masonry building heritage point cloud damage detection damage quantification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 07 May, 2026 Submission checks completed at journal 07 May, 2026 First submitted to journal 05 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9620909","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":641598805,"identity":"29122520-3e63-4b7b-8b99-fd6a793b82ad","order_by":0,"name":"Fan Sun","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Sun","suffix":""},{"id":641598809,"identity":"48daf790-bcf1-4246-8eb6-54fa98151ad6","order_by":1,"name":"Qing Chun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYJACg4SK/3L8zMyHHxCv5cEZZmPJdrY0A6K1MD5sY07ccJ5HQYIo5fztuQcKEtvYEjcf5mEwYKixiSaoReLMuwSDhHM8xtsO8x54wHAsLbeBkBYDiRwDg4QyCdlth/kSDBgbDhOrhc2AcXMzj4EECVraEhQ3MBOrReLMG6CWMweMJQ4DAzmBGL/wt+eYGf6oOCDH33/48IMPNTaEtTAwAD2CYBNWDlbG/IA4haNgFIyCUTBiAQDo9T9X5GPnxAAAAABJRU5ErkJggg==","orcid":"","institution":"Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Chun","suffix":""},{"id":641598819,"identity":"341a06ae-ef73-4811-94ca-615f565f0a04","order_by":2,"name":"Shiyu Ma","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Shiyu","middleName":"","lastName":"Ma","suffix":""},{"id":641598820,"identity":"c63cb266-001c-4d6e-b8ca-7c3f445c64d5","order_by":3,"name":"Yu Yuan","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-05-05 16:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9620909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9620909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109457342,"identity":"3fe407fc-c6b8-452e-9d64-03b7c7fd4ac8","added_by":"auto","created_at":"2026-05-18 10:14:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530184,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9620909/v1_covered_a3fd5698-9815-47fd-82eb-c7c8ab0d0480.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Method for Detecting and Quantifying Damages in Masonry Architectural Heritage Based on Deep Learning of Point Cloud Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"deep learning, masonry building heritage, point cloud, damage detection, damage quantification","lastPublishedDoi":"10.21203/rs.3.rs-9620909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9620909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the development of deep learning, the surface damage detection of masonry architectural heritage in 2D images has achieved remarkable progress. 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