Hybrid Visual Computing Framework for Topology-pre- serving Automated Plot Extraction from Digitized Cadastral Maps

Hybrid Visual Computing Framework for Topology-pre- serving Automated Plot Extraction from Digitized Cadastral Maps | 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 Systematic Review Hybrid Visual Computing Framework for Topology-pre- serving Automated Plot Extraction from Digitized Cadastral Maps Nilesh Fal Dessai, GAURAV NAIK, Aisha Fernandes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9455518/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 Cadastral maps serve as essential infrastructure for land administration and geographic information systems. Legacy scanned maps suffer from noise, distortion, and inefficiency in manual digitization, creating demands for automated extraction solutions. Existing approaches face limitations in boundary integrity , topological correctness, and cross-dataset generalization. This work proposes a hybrid visual computing framework for plot and sub-plot extraction from digitized cadastral maps. The framework integrates deep segmentation and geometric refinement to improve accuracy and topological consistency. Experiments on real-world maps show 89.92% F1-score and 81.68% IoU, outperforming standalone baseline models/methods. This study provides a practical solution for scalable cadastral map digitization. visual computing topology-aware segmentation geometric refine-ment semantic boundary preservation structural map understanding spatial vis-ual representation visual feature learning boundary-aware extraction topology-preserving reconstruction 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-9455518","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":637426781,"identity":"f8a7c96f-0c02-42dc-939a-12cf7162d252","order_by":0,"name":"Nilesh Fal Dessai","email":"","orcid":"","institution":"Goa University","correspondingAuthor":false,"prefix":"","firstName":"Nilesh","middleName":"Fal","lastName":"Dessai","suffix":""},{"id":637426782,"identity":"412ecd43-1782-45ed-a9f7-076961d8ea68","order_by":1,"name":"GAURAV NAIK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDADNh7G9g8fQAx24rUwtzHOADGYibaGh72NmQfEIKRF3v2M2YefbXfk+XgOtj22+bVNno+ZgfHDxxzcWgzP5BjP7G17ZtjG29hunNt327CNmYFZcuY2PFoacowZeM4cZmzjZ2yQzu25zQjUwsbMi09L/xtjxj9nDtuDtVj23LYnqEVeIseYmaficCLQYW3SDD9uJxLUYiDxrJhZpuJwchvPwWbD3obbyW3MjM14/SLfn7yZ8Y3BYdv5PekPH/z4c9t2fnvzwQ8f8dlyAJnH2AYmG3CrB9mCKv0Hr+JRMApGwSgYoQAA7/pPJvt4iucAAAAASUVORK5CYII=","orcid":"","institution":"Goa University","correspondingAuthor":true,"prefix":"","firstName":"GAURAV","middleName":"","lastName":"NAIK","suffix":""},{"id":637426783,"identity":"c955e13f-c09b-4b1b-8da3-64d502c8efca","order_by":2,"name":"Aisha Fernandes","email":"","orcid":"","institution":"Goa University","correspondingAuthor":false,"prefix":"","firstName":"Aisha","middleName":"","lastName":"Fernandes","suffix":""}],"badges":[],"createdAt":"2026-04-18 08:39:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9455518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9455518/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109092990,"identity":"03e5860d-5e92-43d1-b6f0-0f881e05011e","added_by":"auto","created_at":"2026-05-12 13:44:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993324,"visible":true,"origin":"","legend":"","description":"","filename":"TheVCJournal06052026ver3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9455518/v1_covered_4cef8e42-05b8-4711-be86-3d3e50a291a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHybrid Visual Computing Framework for Topology-pre- serving Automated Plot Extraction from Digitized Cadastral Maps\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"visual computing, topology-aware segmentation, geometric refine-ment, semantic boundary preservation, structural map understanding, spatial vis-ual representation, visual feature learning, boundary-aware extraction, topology-preserving reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-9455518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9455518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cadastral maps serve as essential infrastructure for land administration and geographic information systems. Legacy scanned maps suffer from noise, distortion, and inefficiency in manual digitization, creating demands for automated extraction solutions. Existing approaches face limitations in boundary integrity , topological correctness, and cross-dataset generalization. This work proposes a hybrid visual computing framework for plot and sub-plot extraction from digitized cadastral maps. The framework integrates deep segmentation and geometric refinement to improve accuracy and topological consistency. Experiments on real-world maps show 89.92% F1-score and 81.68% IoU, outperforming standalone baseline models/methods. This study provides a practical solution for scalable cadastral map digitization.","manuscriptTitle":"Hybrid Visual Computing Framework for Topology-pre- serving Automated Plot Extraction from Digitized Cadastral Maps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 12:39:44","doi":"10.21203/rs.3.rs-9455518/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":"c6d77b1a-bf1b-45c7-83fb-8a8d158f09f2","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T12:39:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 12:39:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9455518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9455518","identity":"rs-9455518","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}