KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring | 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 KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring Saad Sikander, Hamood Ur Rehman, Nazia Perwaiz, Waqar Aslam, Fakhr Abbas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308154/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 Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures—specifically, Ki-UNet and Attention UNet—and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://github.com/SaadBaloch96/Dataset/blob/main/README.md) Remote sensing water bodies segmentation Attention UNet Ki-UNet ensemble 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-4308154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294557678,"identity":"de94ff1c-fb61-4585-96ca-c2069c2dbdbf","order_by":0,"name":"Saad Sikander","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYJACCcYGBhkGBh6GAwkVNkA+Y+MBYrTwALUwPvhwJg2kpYFoLcyGM9sOg0XwapFvP/zwBuMOOx5+9rPHpHnbztutbT8MtKXGJhqXFoMzacYWjGeSeSR78tKkec7dTt52JhGo5VhabgMuLQwJZhKMbcw8Bjd4zKR5ym4nmx0AamFsOIxTi3z/829ALfVQLWznks3OP8SvheFGDsiWwyAtxoYz2g7Ymd0gYIvBjTfFFolnjgP9kmMIDOTkBLMbQFsS8PhFvj99442PO6rl+NnPGACj0s7e7Hz6wwcfamxwOwwEEpDYiQ3oIgSBPSmKR8EoGAWjYGQAAN4CYxoxB39VAAAAAElFTkSuQmCC","orcid":"","institution":"Sir Syed CASE Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Saad","middleName":"","lastName":"Sikander","suffix":""},{"id":294557679,"identity":"06f60820-961f-4989-b0e6-1679e9a8b4fa","order_by":1,"name":"Hamood Ur Rehman","email":"","orcid":"","institution":"Sir Syed CASE Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hamood","middleName":"Ur","lastName":"Rehman","suffix":""},{"id":294557680,"identity":"c2d53b0a-9d11-4e89-89c7-fc8a1e936a3f","order_by":2,"name":"Nazia Perwaiz","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nazia","middleName":"","lastName":"Perwaiz","suffix":""},{"id":294557681,"identity":"853715de-ecaf-47bf-b76a-bf073b80b8b7","order_by":3,"name":"Waqar Aslam","email":"","orcid":"","institution":"Sir Syed CASE Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Waqar","middleName":"","lastName":"Aslam","suffix":""},{"id":294557682,"identity":"4e8c5b99-e89c-4775-955d-6d6ca82a5dd2","order_by":4,"name":"Fakhr Abbas","email":"","orcid":"","institution":"Sir Syed CASE Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Fakhr","middleName":"","lastName":"Abbas","suffix":""}],"badges":[],"createdAt":"2024-04-22 21:09:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4308154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55955396,"identity":"a18a46b5-4322-4585-a535-24b1eeecfff1","added_by":"auto","created_at":"2024-05-06 20:03:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1049257,"visible":true,"origin":"","legend":"","description":"","filename":"Paper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308154/v1_covered_d852089a-2198-4abd-b6b2-c6dc1042bebf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring","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":"Remote sensing, water bodies segmentation, Attention UNet, Ki-UNet, ensemble","lastPublishedDoi":"10.21203/rs.3.rs-4308154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMonitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures—specifically, Ki-UNet and Attention UNet—and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://github.com/SaadBaloch96/Dataset/blob/main/README.md)\u003c/p\u003e","manuscriptTitle":"KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 22:06:22","doi":"10.21203/rs.3.rs-4308154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"06ff4f65-711c-4b2f-9121-0b88caee88ff","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-06T19:54:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 22:06:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4308154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4308154","identity":"rs-4308154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}