Use of low-cost drones to map arbovirus vector habitats with multispectral aerial imagery

Abstract

Abstract Background This article introduces WaterMAI, a novel multispectral aerial imagery dataset that is optimized for detecting small to medium water bodies and is essential for mapping arbovirus vector habitats. While satellite datasets provide broad coverage and are valuable in many contexts, WaterMAI concentrates on utilizing high-resolution aerial imagery. This approach is suitable for capturing detailed information about water bodies, which may contain vectors for arboviruses. Materials and methods We benchmarked baseline deep learning algorithms on our WaterMAI dataset for water body detection, employing both bounding box and segmentation approaches, establishing new baselines for this domain. Furthermore, we extensively investigate the effectiveness of various spectral band combinations, including Near-infrared (NIR), Red, Green, Blue (RGB), and the Normalized Difference Water Index (NDWI), to determine the potential configuration for accurate water body detection. Results The WaterMAI dataset, covering 16 rural and sub-tropical regions with varied water bodies, increases the utility of research through multiple spectral bands, including visible and near-infrared. The findings demonstrate the potential of multispectral imagery that shall enhance the understanding and monitoring of water bodies in rural and subtropical regions. The WaterMAI dataset, orthomosaic images, and the implementation of the segmentation models for benchmarking are available in GitHub database. Conclusion Our result suggests incorporating NDWI and NIR spectral bands with RGB images potentially improves the water body detection algorithm.