Deep learning identifies water bodies from low-cost drone images for mosquito larval habitat mapping

Mosquito larval source management remains a critical strategy for malaria control, but traditional methods for mapping breeding sites are labor-intensive and slow. While drones offer a promising tool, their application is often hindered by reliance on costly orthomosaic generation (stitching drone images into precise, georeferenced maps) and multispectral sensors, creating significant barriers for resource-limited health programs. This study developed an alternative workflow using a deep learning approach for water body segmentation. We utilized the DeepLabV3 + architecture with an EfficientNetV2 backbone on still RGB and Grayscale drone imagery, bypassing the need for orthomosaics by directly georeferencing individual images using GPS metadata. The model was trained and evaluated on a dataset of over 4,400 images from Pangandaran Regency, Indonesia, with performance assessed via the Intersection over Union (IoU) metric. Predictions were validated through field inspections to confirm water presence and larval abundance. The model achieved a mean IoU of 0.86 on RGB images and 0.80 on Grayscale data, demonstrating robust performance even with minimal spectral information. Field validation of 47 predicted sites confirmed water presence in all cases (100% confirmation of water presence), with 31.9% found to contain mosquito larvae, including the primary local vectors Anopheles vagus and An. sundaicus. The entire workflow, from cloud-based processing to field localization, was executed without specialized hardware or software. Our findings demonstrate that a deep learning-based analysis of still drone imagery provides a scalable, cost-effective, and rapid alternative to multispectral and orthomosaic-dependent methods for larval habitat mapping. This approach democratizes advanced surveillance technology, significantly shortens the delay between data collection and intervention, and holds great promise for enhancing the efficiency of vector control programs in malaria-endemic regions.