Ground Truthing of Dumpsites using Remote Sensing and Machine Learning Approaches in Peri-Urban Settings

Ground Truthing of Dumpsites using Remote Sensing and Machine Learning Approaches in Peri-Urban Settings | 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 Ground Truthing of Dumpsites using Remote Sensing and Machine Learning Approaches in Peri-Urban Settings Veena Bhajantri, Ashootosh S. Mandpe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7140963/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The open dumping of waste poses severe environmental and public health hazards when exposed to the atmosphere. Therefore, to reduce these consequences, it is crucial to identify waste disposal sites across large areas. However, the local government agencies and environmental groups often pose significant challenges in obtaining the information on dumpsite location data promptly. Hence, the present study focused on the detection of the existing dumpsites in Madhya Pradesh using the Random Forest (RF) machine learning technique with the use of Sentinel-2 images for the year 2022. The logistic regression function was then implemented to analyse the influence of Land surface temperature (LST), Normalized Differential Vegetation Index (NDVI), and Normalized Differential Built-up Index (NDBI) on classified dumpsite features. The RF technique achieved an overall accuracy of 86.49%. The LST, NDVI, and NDBI values were extracted for the 37 sample datasets. The extracted temperatures for dump sites vary from 35.47 to 39.58 ℃, whereas the NDVI and NDBI range between 0.04–0.25 and − 0.06 to 0.12, respectively. Subsequently, the overall accuracy of logistic regression obtained was 88%, showing the collective findings of LST, NDVI, and NDBI demonstrate a significant contribution to the dumpsite detection. Using this novel approach, 60 undocumented dumpsites were successfully detected. The developed methodology effectively detects dumpsite locations; however, it lags in analyzing the morphological and compositional attributes of such areas. Therefore, the further studies will focus on integrating the field investigations with high-resolution remote sensing data to assess characteristics of the identified dumpsite locations. Dumpsite detection Environmental monitoring Land surface temperature Logistic regression Random Forest algorithm Remote sensing indices Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Feb, 2026 Reviews received at journal 29 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 16 Jul, 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. 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. 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Settings","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dumpsite detection, Environmental monitoring, Land surface temperature, Logistic regression, Random Forest algorithm, Remote sensing indices","lastPublishedDoi":"10.21203/rs.3.rs-7140963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7140963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe open dumping of waste poses severe environmental and public health hazards when exposed to the atmosphere. Therefore, to reduce these consequences, it is crucial to identify waste disposal sites across large areas. However, the local government agencies and environmental groups often pose significant challenges in obtaining the information on dumpsite location data promptly. Hence, the present study focused on the detection of the existing dumpsites in Madhya Pradesh using the Random Forest (RF) machine learning technique with the use of Sentinel-2 images for the year 2022. The logistic regression function was then implemented to analyse the influence of Land surface temperature (LST), Normalized Differential Vegetation Index (NDVI), and Normalized Differential Built-up Index (NDBI) on classified dumpsite features. The RF technique achieved an overall accuracy of 86.49%. The LST, NDVI, and NDBI values were extracted for the 37 sample datasets. The extracted temperatures for dump sites vary from 35.47 to 39.58 ℃, whereas the NDVI and NDBI range between 0.04\u0026ndash;0.25 and \u0026minus;\u0026thinsp;0.06 to 0.12, respectively. Subsequently, the overall accuracy of logistic regression obtained was 88%, showing the collective findings of LST, NDVI, and NDBI demonstrate a significant contribution to the dumpsite detection. Using this novel approach, 60 undocumented dumpsites were successfully detected. The developed methodology effectively detects dumpsite locations; however, it lags in analyzing the morphological and compositional attributes of such areas. Therefore, the further studies will focus on integrating the field investigations with high-resolution remote sensing data to assess characteristics of the identified dumpsite locations.\u003c/p\u003e","manuscriptTitle":"Ground Truthing of Dumpsites using Remote Sensing and Machine Learning Approaches in Peri-Urban Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 08:16:37","doi":"10.21203/rs.3.rs-7140963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-12T10:36:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T06:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338354126601620544619035734668371501138","date":"2025-11-27T04:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283761301767540842857606615973857855233","date":"2025-11-27T00:34:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T07:51:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T01:45:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-31T01:45:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Geomatics","date":"2025-07-16T14:13:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"138662fb-d302-4181-ab5a-ad0886a9472b","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T13:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 08:16:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7140963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7140963","identity":"rs-7140963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}