A Low-Cost and Scalable Landslide Monitoring and Early Warning System for Mountainous Regions Using Deep Learning-based Computer Vision

A Low-Cost and Scalable Landslide Monitoring and Early Warning System for Mountainous Regions Using Deep Learning-based Computer Vision | 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 A Low-Cost and Scalable Landslide Monitoring and Early Warning System for Mountainous Regions Using Deep Learning-based Computer Vision Jie Chen, Longfei Song, Yuanyuan Pu, Derek Apel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5744473/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 Landslides pose severe risks to lives, property, and infrastructure, particularly in mountainous regions where monitoring is hindered by harsh environmental conditions and complex terrains. This study proposes a scalable, low-cost landslide monitoring and early warning system that integrates deep learning and computer vision technologies. Using a single optical camera, time-lapse images of slopes were captured and processed through advanced alignment, masking, and histogram equalization to maintain accuracy under adverse conditions. The system combines static monitoring via image differencing with dynamic monitoring using a RAFT-based optical flow model to detect surface deformations and real-time displacements. Field tests conducted on Washington Makahdiot Cliff demonstrated the system’s capability to identify landslide risks across three deformation phases. Static analysis detected significant grayscale changes (> 100) linked to actual slope deformations, while dynamic monitoring achieved a displacement detection accuracy of 94.6% for movements up to 25 cm. A novel early warning algorithm, based on thresholds for abnormal pixel changes, successfully classified risk levels, with landslide risk coefficients exceeding 0.4 during landslide events and remaining below 0.1 otherwise. The system proved resilient to environmental challenges, such as fog and strong winds, and demonstrated a positive correlation between landslide risk coefficients and rainfall, underscoring the importance of integrating real-time environmental data. This method provides an effective, robust, and cost-efficient solution for landslide monitoring and mitigation in complex terrains. Landslides detection Computer vision Optical flow Deep learning Full Text 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. 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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-5744473","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405611630,"identity":"ead20a48-ec8e-4b0f-b268-974d89ffbb03","order_by":0,"name":"Jie Chen","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""},{"id":405611631,"identity":"b20b5e92-4a2f-423a-805f-e76262ce443d","order_by":1,"name":"Longfei Song","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Longfei","middleName":"","lastName":"Song","suffix":""},{"id":405611632,"identity":"f6f685a4-876d-47b9-aac5-93260448d352","order_by":2,"name":"Yuanyuan Pu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYHAD5gMQ+gDxWtgSSNbCY0CcFoMbyc8e87bZ5ZlL5Hz88LaNQY7vRgLj5wK8WtLMjXnbkostZ+RulpzbxmAseSOBWXoGXi0JZtK8bcyJG27kbmPmbWMAMhLYmHnwakn/BtRSD1SZ8wykpZ4ILTkgWw6DtLCBtCQYENIieeZNmeScc8cTd/Y8MwYyJAxnnnnYLI1PC9/x9G0Sb8qqE7ezJz/88KbMRp7vePLBz/i0KBxgYGACKQBHCg+DBJBkbMCjgYFBHijN+AOhZRSMglEwCkYBJgAAMf1NQQdllJ4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4548-2691","institution":"Chongqing University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Pu","suffix":""},{"id":405611633,"identity":"78d8603f-3135-49d8-9c38-c02189362d64","order_by":3,"name":"Derek Apel","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Derek","middleName":"","lastName":"Apel","suffix":""}],"badges":[],"createdAt":"2025-01-01 04:34:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5744473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5744473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86407189,"identity":"fa249513-718e-47ba-8c61-969778858248","added_by":"auto","created_at":"2025-07-10 09:58:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3175093,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript0101.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5744473/v1_covered_694ec6a8-6df1-4037-95a7-a73a6958d40c.pdf"}],"financialInterests":"","formattedTitle":"A Low-Cost and Scalable Landslide Monitoring and Early Warning System for Mountainous Regions Using Deep Learning-based Computer Vision","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":"Landslides detection, Computer vision, Optical flow, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5744473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5744473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandslides pose severe risks to lives, property, and infrastructure, particularly in mountainous regions where monitoring is hindered by harsh environmental conditions and complex terrains. This study proposes a scalable, low-cost landslide monitoring and early warning system that integrates deep learning and computer vision technologies. Using a single optical camera, time-lapse images of slopes were captured and processed through advanced alignment, masking, and histogram equalization to maintain accuracy under adverse conditions. The system combines static monitoring via image differencing with dynamic monitoring using a RAFT-based optical flow model to detect surface deformations and real-time displacements. Field tests conducted on Washington Makahdiot Cliff demonstrated the system\u0026rsquo;s capability to identify landslide risks across three deformation phases. Static analysis detected significant grayscale changes (\u0026gt;\u0026thinsp;100) linked to actual slope deformations, while dynamic monitoring achieved a displacement detection accuracy of 94.6% for movements up to 25 cm. A novel early warning algorithm, based on thresholds for abnormal pixel changes, successfully classified risk levels, with landslide risk coefficients exceeding 0.4 during landslide events and remaining below 0.1 otherwise. The system proved resilient to environmental challenges, such as fog and strong winds, and demonstrated a positive correlation between landslide risk coefficients and rainfall, underscoring the importance of integrating real-time environmental data. 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