Detection of small water bodies for vector control using deep learning on multispectral imagery from unmanned aerial vehicles

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.