Detection of Small Water Bodies for Vector Control Using Deep Learning on Unmanned Aerial Vehicle Multispectral Imagery

Detection of Small Water Bodies for Vector Control Using Deep Learning on Unmanned Aerial Vehicle Multispectral Imagery | 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 Detection of Small Water Bodies for Vector Control Using Deep Learning on Unmanned Aerial Vehicle Multispectral Imagery Phuc Linh Ngo, Viet Hoang Pham, Ngoc Long Bui, Huynh Anh Thu Phan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5622317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 6 You are reading this latest preprint version Abstract Vector-borne diseases pose a persistent public health challenge in tropical regions such as Vietnam, where traditional ground-based surveillance methods struggle with scale and accuracy. This study presents a framework that integrates Unmanned Aerial Vehicle (UAV) multispectral imagery with deep learning techniques to detect small-to-medium-sized water bodies, important habitats for arbovirus vectors. High-resolution multispectral images were captured with the DJI Phantom 4 (P4M) Multispectral UAV in rural and peri-urban areas of Binh Duong province in Vietnam. A curated dataset of 982 annotated images was created, comprising RGB, near-infrared (NIR), and normalized difference water index (NDWI) bands. Six state-of-the-art object detection and segmentation models were evaluated, including YOLOv7, YOLOv7x, DocF, U-Net, MSNet, and RTFNet. Among them, segmentation models (U-Net and MSNet) using RGB + Green + NIR + NDWI achieved the best performance with dice scores above 0.92. The results show that the combination of UAV multispectral imagery with deep learning significantly improves the detection accuracy of water bodies in complex tropical conditions. This approach provides a scalable, cost-effective solution for mapping small water bodies and contributes to targeted vector control and disease prevention measures in arbovirus-prone regions. UAV Multispectral Imagery Water Body Detection Arbovirus Vector Deep Learning Vietnam Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 24 Apr, 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. 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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-5622317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449821777,"identity":"e3dbf2fe-b935-4f23-a8df-1a31cf3f20d4","order_by":0,"name":"Phuc Linh Ngo","email":"","orcid":"","institution":"Vietnamese-German University","correspondingAuthor":false,"prefix":"","firstName":"Phuc","middleName":"Linh","lastName":"Ngo","suffix":""},{"id":449821778,"identity":"fff8fce4-def2-4a10-8cbc-672cc2134ba1","order_by":1,"name":"Viet Hoang Pham","email":"","orcid":"","institution":"Vietnamese-German University","correspondingAuthor":false,"prefix":"","firstName":"Viet","middleName":"Hoang","lastName":"Pham","suffix":""},{"id":449821779,"identity":"8166186a-5fce-49b5-a563-009ceace953f","order_by":2,"name":"Ngoc Long Bui","email":"","orcid":"","institution":"Vietnamese-German University","correspondingAuthor":false,"prefix":"","firstName":"Ngoc","middleName":"Long","lastName":"Bui","suffix":""},{"id":449821780,"identity":"68cacf12-c4c2-4f27-8c43-d2e1245367b4","order_by":3,"name":"Huynh Anh Thu Phan","email":"","orcid":"","institution":"Vietnamese-German University","correspondingAuthor":false,"prefix":"","firstName":"Huynh","middleName":"Anh Thu","lastName":"Phan","suffix":""},{"id":449821781,"identity":"3bbb4bc3-5fa5-43c5-9c39-bb2c25a4923c","order_by":4,"name":"Bich Hien Vo","email":"","orcid":"","institution":"Vietnamese-German University","correspondingAuthor":false,"prefix":"","firstName":"Bich","middleName":"Hien","lastName":"Vo","suffix":""},{"id":449821782,"identity":"1186d87d-84f6-4fd0-8cea-12c21f88d1b5","order_by":5,"name":"Thirumalaisamy P. 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This study presents a framework that integrates Unmanned Aerial Vehicle (UAV) multispectral imagery with deep learning techniques to detect small-to-medium-sized water bodies, important habitats for arbovirus vectors. High-resolution multispectral images were captured with the DJI Phantom 4 (P4M) Multispectral UAV in rural and peri-urban areas of Binh Duong province in Vietnam. A curated dataset of 982 annotated images was created, comprising RGB, near-infrared (NIR), and normalized difference water index (NDWI) bands. Six state-of-the-art object detection and segmentation models were evaluated, including YOLOv7, YOLOv7x, DocF, U-Net, MSNet, and RTFNet. Among them, segmentation models (U-Net and MSNet) using RGB\u0026thinsp;+\u0026thinsp;Green\u0026thinsp;+\u0026thinsp;NIR\u0026thinsp;+\u0026thinsp;NDWI achieved the best performance with dice scores above 0.92. 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