An object-oriented and parameter fine-tuning framework for post-earthquake building damage assessment

An object-oriented and parameter fine-tuning framework for post-earthquake building damage assessment | 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 An object-oriented and parameter fine-tuning framework for post-earthquake building damage assessment Rui Yang, Yuan Qi, Jinlong Zhang, Hongwei Wang, Juan Zhang, Lu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7297034/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Earthquakes present a significant threat to human life and infrastructure, highlighting the urgent need for rapid and accurate building damage assessments to inform disaster response efforts. High Spatial Resolution (HSR) remote sensing images, which offer detailed surface information, are indispensable for such assessments. However, existing methods, especially those based on Convolutional Neural Networks (CNNs), frequently fail to capture global correlations between local image patches, reducing their effectiveness in densely populated urban areas. This gap underscores the need for a more robust approach that integrates global context with local features. Here, we present S2AC-Net, a novel framework for post-earthquake building damage assessment. In this framework, the Segment Anything Model (SAM) replaces traditional multi-resolution segmentation in processing pre-disaster images by utilizing frozen image encoding parameters and adapters within a Vision Transformer structure to predict building probabilities. Building delineation is achieved by merging segmentation results with probability predictions, and spectral and texture features from pre- and post-disaster images are mapped onto building regions to construct feature vectors. These vectors, when combined with field survey data, enable damage level assessment using CNNs. When applied to the December 2023 Jishishan earthquake, S2AC-Net achieved a building localization accuracy of 0.928 and an overall accuracy of 0.882. These results demonstrate S2AC-Net's effectiveness in overcoming the limitations of traditional CNNs, advancing the field of remote sensing-based disaster assessment, and providing a scalable solution for urban resilience planning. Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Earthquake Building damage assessment High Spatial Resolution Deep learning parameter fine-tuning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7297034","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502405361,"identity":"3b3aa901-6199-4fe1-9801-44ef6e9b3d38","order_by":0,"name":"Rui Yang","email":"","orcid":"","institution":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yang","suffix":""},{"id":502405362,"identity":"0efed430-9338-40dc-9dec-8f5ae862dc00","order_by":1,"name":"Yuan 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High Spatial Resolution (HSR) remote sensing images, which offer detailed surface information, are indispensable for such assessments. However, existing methods, especially those based on Convolutional Neural Networks (CNNs), frequently fail to capture global correlations between local image patches, reducing their effectiveness in densely populated urban areas. This gap underscores the need for a more robust approach that integrates global context with local features. Here, we present S2AC-Net, a novel framework for post-earthquake building damage assessment. In this framework, the Segment Anything Model (SAM) replaces traditional multi-resolution segmentation in processing pre-disaster images by utilizing frozen image encoding parameters and adapters within a Vision Transformer structure to predict building probabilities. Building delineation is achieved by merging segmentation results with probability predictions, and spectral and texture features from pre- and post-disaster images are mapped onto building regions to construct feature vectors. These vectors, when combined with field survey data, enable damage level assessment using CNNs. When applied to the December 2023 Jishishan earthquake, S2AC-Net achieved a building localization accuracy of 0.928 and an overall accuracy of 0.882. These results demonstrate S2AC-Net's effectiveness in overcoming the limitations of traditional CNNs, advancing the field of remote sensing-based disaster assessment, and providing a scalable solution for urban resilience planning.\u003c/p\u003e","manuscriptTitle":"An object-oriented and parameter fine-tuning framework for post-earthquake building damage assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 14:59:44","doi":"10.21203/rs.3.rs-7297034/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9929f6d8-5f4e-48ca-8081-e27ce1e2b97f","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53365673,"name":"Physical sciences/Mathematics and computing"},{"id":53365674,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-10-08T14:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 14:59:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7297034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7297034","identity":"rs-7297034","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}