Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments

Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments | 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 Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments Ibne Farabi Shihab, Benjir Islam Alvee, Sudesh Ramesh Bhagat, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3793930/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate sidewalk detection is crucial in improving road safety and urban planning. The study evaluated the model’s performance on Cityscapes, Ade20k, and the Boston Dataset. The results showed that the ensemble model performed better than the individual models, achieving mean Intersection Over Union (mIOU) scores of 93.1%, 90.3%, and 90.6% on these datasets under ideal conditions. Additionally, the ensemble model maintained a consistent level of performance even in challenging conditions such as Salt-and-Pepper and Speckle noise, with only a gradual decrease in efficiency observed. On the other hand, the ONE-PEACE LLM performed slightly better than the ensemble model in ideal scenarios but experienced a significant decline in performance under noisy conditions. These findings demonstrate the robustness and reliability of the ensemble model, making it a valuable asset for improving urban infrastructure related to road safety and curb space management. This study contributes positively to the broader context of urban health and mobility. Sidewalk Ensemble method Robustness LLM Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3793930","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263861931,"identity":"46b61477-d425-4354-924f-bb2690c5191f","order_by":0,"name":"Ibne Farabi Shihab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNgYehsMMDAfk2BBiCcRpMQZqYWyAqCaghQGohRmoJbGBaC18/GcPHi6ouZPex378+YOfP2wY+NlzDPA7TCIv4fCMY89y23hyDBt7EtIYJHveENLCY3CYh+1wbhtDDmMDT8JhBoMbhGzhPwPU8u9wOhv/84eNfxL+M9gT1MKQY3CYt+1wAptEgmEzT8IBBgMJgn4Bael7Ztgm8cZwtkxaMo/EmWcFeLXI958x/szz7Y68fH/6g49vbOzk+NuTN+DVggF4SFM+CkbBKBgFowArAACUj0a+iw3q4gAAAABJRU5ErkJggg==","orcid":"","institution":"Iowa State University","correspondingAuthor":true,"prefix":"","firstName":"Ibne","middleName":"Farabi","lastName":"Shihab","suffix":""},{"id":263861932,"identity":"20a985f1-c7b0-4f46-9b2d-6f33f4363866","order_by":1,"name":"Benjir Islam Alvee","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Benjir","middleName":"Islam","lastName":"Alvee","suffix":""},{"id":263861933,"identity":"46436308-0092-4c7c-87d5-6f5a2a9053d8","order_by":2,"name":"Sudesh Ramesh Bhagat","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Sudesh","middleName":"Ramesh","lastName":"Bhagat","suffix":""},{"id":263861934,"identity":"f884ce4a-f048-4dd1-b578-6f4688cfdd89","order_by":3,"name":"Anuj Sharma","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Anuj","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2023-12-22 21:44:07","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3793930/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3793930/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53893051,"identity":"fd9578e2-53f7-4743-9e3a-f765cbe6bd23","added_by":"auto","created_at":"2024-04-01 22:44:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841061,"visible":true,"origin":"","legend":"","description":"","filename":"SensorsPreciseandRobustSidewalkDetectionLeveragingEnsembleLearningtoSurpassLLMLimitationsinUrbanEnvironmentsclean.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793930/v2_covered_cb9e543c-0e14-42fe-a67b-a17865d1682d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments","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":"Sidewalk, Ensemble method, Robustness, LLM","lastPublishedDoi":"10.21203/rs.3.rs-3793930/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3793930/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. 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