Research on landslide detection method based on improved VMamba-UNet+

Research on landslide detection method based on improved VMamba-UNet+ | 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Article Research on landslide detection method based on improved VMamba-UNet+ Ankang Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8869617/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 As remote sensing technology improves, the segmentation of landslide targets has become more important in disaster prevention, control, and urban construction, and it plays a significant role in disaster loss evaluation and post-disaster rescue. Therefore, this paper presents an improved UNet-based landslide segmentation algorithm. China has a large proportion of land in landslide-prone areas, and remote sensing technologies are becoming a preferred method for investigating and monitoring landslides. Rapid and precise recognition of landslide-prone regions through high-resolution remote sensing images is essential for disaster reduction. Although Convolutional Neural Networks have made progress in automatic detection, they have small receptive fields which can't capture long-range spatial dependencies. Conversely, Transformer-based methods provide global modeling capability but suffer from quadratic computational complexity, making it difficult to implement real-time processing. In order to solve such problems, this paper puts forward VMamba-UNet+, a new kind of light-weighted landslide detection network based on Visual State Space Model (VMamba). It uses CSM to get global features with linear computation cost. Additionally, it includes Dynamic Snake Convolution (DSC) to improve the extraction of features from irregular landslide boundaries, as well as the Spatial Group-wise Enhance (SGE) module to efficiently suppress complex background noise. On the landslide dataset, VMamba-UNet+ gets an mIoU of 0.8716 and an F1-score of 0.9278. These numbers show that the suggested model keeps low computing costs and does a great job at separating things, giving a clear and not using much power way to find landslides fast. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Landslide detection VMamba DSC SGE Lightweight networking Remote sensing image segmentation 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-8869617","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593030900,"identity":"9377de9b-20c6-4f38-85b0-191ed7380243","order_by":0,"name":"Ankang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3RoQrCUBTG8XsRpmGaFeHuCYQzDggWn8WLoFWb8VwGWvYAC77FYNXJYGn2NbVoWZgYFRRMmnZtgvfffxwOH2Mm0w/m1BVBCZbglMR6xPUTmgezFtZUOtI8k0/p2iiFXHkZ6AlOUoUM+thcZpe8YEPRowpSZ1sPZzARHX8XDtZsjP246opSSwwgRTfYRV2bxTKqIizhq5sND7nZFydNknJCGyypKLP0iOtzcgOwkFOKgzVo/OI45wOU99eUx7xYDEUl+ahta07zTr4VJpPJ9Bc9AZC5SVcU0JnwAAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ankang","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-02-13 08:54:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8869617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8869617/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105700206,"identity":"3c176e0c-9b26-4333-862e-7e3f3e4101c1","added_by":"auto","created_at":"2026-03-30 05:42:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4727915,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonlandslidedetectionmethodbasedonimprovedVMambaUNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8869617/v1_covered_53a97e5e-45c9-41e2-9f87-a93ca742fd59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on landslide detection method based on improved VMamba-UNet+","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Landslide detection, VMamba, DSC, SGE, Lightweight networking, Remote sensing image segmentation","lastPublishedDoi":"10.21203/rs.3.rs-8869617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8869617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs remote sensing technology improves, the segmentation of landslide targets has become more important in disaster prevention, control, and urban construction, and it plays a significant role in disaster loss evaluation and post-disaster rescue. 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