Hybrid GeoAI Utilizing DQN-Driven Adaptive Fusion Approach for Approximate Polygon Regularisation of Rasterised Building Footprints

Hybrid GeoAI Utilizing DQN-Driven Adaptive Fusion Approach for Approximate Polygon Regularisation of Rasterised Building Footprints | 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 Research Article Hybrid GeoAI Utilizing DQN-Driven Adaptive Fusion Approach for Approximate Polygon Regularisation of Rasterised Building Footprints vibhor joshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9378815/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 Acquiring accurate building footprints from remote sensing images across a continental scale presents significant challenges due to data variability, insufficient labelling, and computational limitations. We present a GPU-accelerated hybrid GeoAI framework designed to transform rasterised U.S. building data into normalised 3-channel patches (area, count, duplicated area) accompanied by binary masks. The framework consists of an eight-step progressive enhancement pipeline: baseline Mask R-CNN segmentation, GPU acceleration, enhanced regularizers (RT: closing; RR: open-close; FER: edge-augmented), basic RL adaptive fusion, continuous actions via PPO, CNN contextual embeddings, pre-trained models (COCO initialisation), and multi-state training. The pipeline underwent testing on subsets comprising approximately 500 patches sourced from eight U.S. states, utilising the Rasterised Building Footprints for USA dataset. The framework attained a mean IoU of 74.9% (F1 = 0.802), indicating a 7.1% enhancement compared to the baseline Mask R-CNN (67.8%). Although the adaptive reinforcement learning components extend the training duration, the implementation of GPU acceleration and parallelised morphological operations significantly decreases inference time, resulting in a 17.6× speed-up (70.8 ms per patch, 326 patches per minute) when compared to the CPU baseline. Multi-state validation shows a consistent improvement of 4.86% in IoU across different geographies, such as a notable increase of 5.2% in New Hampshire. Ablation studies indicate that the largest contributions come from continuous actions, which account for a 1.4% increase in IoU, and multi-state training, contributing an additional 0.5% to IoU. The platform provides a reproducible test environment for adaptive regularisation, demonstrating improved boundary accuracy and shape retention in qualitative evaluations, while enabling statewide analysis in a matter of hours. Artificial Intelligence and Machine Learning Building footprint extraction Geospatial AI Reinforcement learning Adaptive fusion DQN Mask R-CNN Morphological regularisation (RT RR FER) Raster window sampling Vectorisation Boundary IoU Hausdorff distance Full Text Additional Declarations The authors declare no competing interests. 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-9378815","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620945932,"identity":"9100daea-0944-4656-b462-7fc707e00790","order_by":0,"name":"vibhor joshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsQrCMBCA4ZNAXaK3puhDRASpUPRVFEGXIoKLkwiCk+Da0QdxSAnoorh2dHKuW0EHE63Qpa2jYP4lR7mPJgAm0y9GqPU6ACiIiDOafGffkcBXhBYSeBM9KP46CuKHyv5y20EZ8RRId+LUu7gtRTE440wiq6OGfwVi++Oe9PTF2IXYa2DTLGIvaatGBRAeUp4QATX1lv6ikJyPXDpvQu55BMmHCI9L0AQXVu5fkFSHti+YeovHg7UmYWnVVkMmsfC0Z5FwB4jHZhQ/5t3yRsowns0zSRIbpGa9zPP3dZ3UXUXxuslkMv1VT+l7RbCJFkXfAAAAAElFTkSuQmCC","orcid":"","institution":"indian Institute of information technology guwahati","correspondingAuthor":true,"prefix":"","firstName":"vibhor","middleName":"","lastName":"joshi","suffix":""}],"badges":[],"createdAt":"2026-04-10 11:22:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9378815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9378815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106845121,"identity":"bf40c453-c9cf-4666-a1da-a75987703d6c","added_by":"auto","created_at":"2026-04-14 04:40:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1111549,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9378815/v1_covered_b7bafc76-ca59-422e-b113-fc82852493d7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHybrid GeoAI Utilizing DQN-Driven Adaptive Fusion Approach for Approximate Polygon Regularisation of Rasterised Building Footprints\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"indian institute of information technology guwahati","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":"Building footprint extraction, Geospatial AI, Reinforcement learning, Adaptive fusion, DQN, Mask R-CNN, Morphological regularisation (RT, RR, FER), Raster window sampling, Vectorisation, Boundary IoU, Hausdorff distance","lastPublishedDoi":"10.21203/rs.3.rs-9378815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9378815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcquiring accurate building footprints from remote sensing images across a continental scale presents significant challenges due to data variability, insufficient labelling, and computational limitations. We present a GPU-accelerated hybrid \u0026nbsp;GeoAI framework designed to transform rasterised U.S. building data into normalised 3-channel patches (area, count, duplicated area) accompanied by binary masks. The framework consists of an eight-step progressive enhancement pipeline: baseline Mask R-CNN segmentation, GPU acceleration, enhanced regularizers (RT: closing; RR: open-close; FER: edge-augmented), basic RL adaptive fusion, continuous actions via PPO, CNN contextual embeddings, pre-trained models (COCO initialisation), and multi-state training. The pipeline underwent testing on subsets comprising approximately 500 patches sourced from eight U.S. states, utilising the Rasterised Building Footprints for USA dataset. The framework attained a mean IoU of 74.9% (F1 = 0.802), indicating a 7.1% enhancement compared to the baseline Mask R-CNN (67.8%). Although the adaptive reinforcement learning components extend the training duration, the implementation of GPU acceleration and parallelised morphological operations significantly decreases inference time, resulting in a 17.6× speed-up (70.8 ms per patch, 326 patches per minute) when compared to the CPU baseline. Multi-state validation shows \u0026nbsp;a consistent improvement of 4.86% in IoU across different geographies, such as a notable increase of 5.2% in New Hampshire. Ablation studies indicate that the largest contributions come from continuous actions, which account for a 1.4% increase in IoU, and multi-state training, contributing an additional 0.5% to IoU. The platform provides a reproducible test environment for adaptive regularisation, demonstrating improved boundary accuracy and shape retention in qualitative evaluations, while enabling statewide analysis in a matter of hours.\u003c/p\u003e","manuscriptTitle":"Hybrid GeoAI Utilizing DQN-Driven Adaptive Fusion Approach for Approximate Polygon Regularisation of Rasterised Building Footprints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 04:40:07","doi":"10.21203/rs.3.rs-9378815/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":"662feb33-e213-4599-a6aa-d0e2faab9cc2","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66085067,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-04-14T04:40:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 04:40:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9378815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9378815","identity":"rs-9378815","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}