DE-Net: A Density-Aware and Edge-Enhanced Network for High-Resolution Building Segmentation

DE-Net: A Density-Aware and Edge-Enhanced Network for High-Resolution Building Segmentation | 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 DE-Net: A Density-Aware and Edge-Enhanced Network for High-Resolution Building Segmentation Guanjun Huang, Rui Wu, Liang Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7698414/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 19 You are reading this latest preprint version Abstract High-resolution building extraction from remote sensing images plays a pivotal role in urban planning, disaster response, and land-use monitoring. However, the complex urban environment—characterized by dense building distributions, varied architectural styles, and blurred object boundaries—poses significant challenges for existing semantic segmentation models. To address these issues, we propose DE-Net, a novel semantic segmentation framework designed for precise building extraction from high-resolution unmanned aerial vehicle (UAV) imagery. DE-Net consists of three key components: a ConvNeXt-based feature backbone that captures hierarchical semantic features while preserving spatial detail; a Spatial Resolution Adaptive Reconstruction Module (SRARM) that dynamically decodes features based on predicted density maps, enabling tailored upsampling strategies for dense and sparse regions; and a Multi-Scale Edge-Aware Fusion Module (MS-EAM) that extracts edge attention from each encoder stage and fuses them to enhance boundary localization. Experiments on a high-resolution UAV dataset show that DE-Net outperforms other methods in IoU (86.78%), F1 score (92.91%), and boundary IoU, surpassing the second-best model, UPerNet, by 4.47% and 2.65%, achieving state-of-the-art performance. building segmentation remote sensing adaptive reconstruction edge-aware fusion UAV imagery Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 29 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviews received at journal 27 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 24 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 23 Sep, 2025 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. 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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-7698414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562644895,"identity":"0f15517a-f4f5-4c42-8ea3-449c56336eb4","order_by":0,"name":"Guanjun Huang","email":"","orcid":"","institution":"Huangshan University","correspondingAuthor":false,"prefix":"","firstName":"Guanjun","middleName":"","lastName":"Huang","suffix":""},{"id":562644896,"identity":"540d4a3a-1186-4c17-b24c-13f314bbfab8","order_by":1,"name":"Rui 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Segmentation","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer 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However, the complex urban environment\u0026mdash;characterized by dense building distributions, varied architectural styles, and blurred object boundaries\u0026mdash;poses significant challenges for existing semantic segmentation models. To address these issues, we propose DE-Net, a novel semantic segmentation framework designed for precise building extraction from high-resolution unmanned aerial vehicle (UAV) imagery. DE-Net consists of three key components: a ConvNeXt-based feature backbone that captures hierarchical semantic features while preserving spatial detail; a Spatial Resolution Adaptive Reconstruction Module (SRARM) that dynamically decodes features based on predicted density maps, enabling tailored upsampling strategies for dense and sparse regions; and a Multi-Scale Edge-Aware Fusion Module (MS-EAM) that extracts edge attention from each encoder stage and fuses them to enhance boundary localization. Experiments on a high-resolution UAV dataset show that DE-Net outperforms other methods in IoU (86.78%), F1 score (92.91%), and boundary IoU, surpassing the second-best model, UPerNet, by 4.47% and 2.65%, achieving state-of-the-art performance. \u003c/p\u003e","manuscriptTitle":"DE-Net: A Density-Aware and Edge-Enhanced Network for High-Resolution Building Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 02:43:38","doi":"10.21203/rs.3.rs-7698414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T19:35:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T10:38:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T02:43:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T05:45:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T08:31:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179307582571261162891304507145442934228","date":"2025-12-24T07:09:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127826189394055822821619310540691947882","date":"2025-12-22T00:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98451913874535809237179499753443250156","date":"2025-12-19T13:09:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326574133625609102763938053435624640171","date":"2025-12-19T01:00:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282831416159734057192396466053764986810","date":"2025-12-19T00:16:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333964085738628072501109703839133953317","date":"2025-12-18T17:35:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142206327514728934741697340267168919288","date":"2025-12-18T15:34:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232136536972315261288917391939247315916","date":"2025-12-17T01:22:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332679284232016763846509940001745412832","date":"2025-12-17T00:21:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34280621137509864499610757726554695478","date":"2025-12-16T22:46:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-16T20:12:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T19:48:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T04:10:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2025-09-24T01:40:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b57e15a9-51a5-489b-b101-7b8271232d40","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T19:24:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 02:43:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7698414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7698414","identity":"rs-7698414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}