Abstract
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.
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Acknowledgements
We would like to thank Akbal Thufail and Nanang Sulistyono for their assistance and support for field surveys during drone flights and water bodies inspection.
Funding
This study was supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) to a project on Joint Usage/Research Center, Leading Academia in Marine and Environment Pollution Research (LaMer), the Japan Society for the Promotion of Science (JSPS) Core-to-Core Program B. Asia-Africa Science Platforms (grant number JPJSCCB20240008), JSPS Bilateral Joint Research Projects (Open Partnership) with Indonesia (grant number 120249930), and the Japan International Cooperation Agency and the Japan Agency for Medical Research and Development through the Science and Technology Research Partnership for Sustainable Development (JICA-AMED SATREPS; grant number JP24jm0110032).
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Francisco, M.E., Ruliansyah, A., Pradani, F.Y. et al. Deep learning identifies water bodies from low-cost drone images for mosquito larval habitat mapping.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-58240-4
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DOI: https://doi.org/10.1038/s41598-026-58240-4
Keywords
- Malaria
- Orthomosaic
- Larval source management
- Water segmentation
- Vector control
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