Leveraging drone imaging and AI computer vision to improve Aedes aegypti vector control programs

Discarded tires, plastic bottles, and piles of household trash create breeding sites for mosquitoes seeking shallow water-holding containers to lay their eggs. Aedes aegypti mosquitoes are the primary vectors that transmit dengue, chikungunya, and Zika – three diseases with no specific treatment and no widely available vaccine – causing over 400 million dengue infections alone annually. Finding these piles can be slow, and they are often hidden from view. This project develops a drone-AI surveillance system that rapidly identifies mosquito breeding sites in discarded tires and trash across urban neighborhoods. Using high-resolution aerial imagery and computer vision, the system maps breeding “hotspots” that traditional ground surveillance often misses. By enabling precision targeting of vector control interventions, this tool improves public health in resource-limited settings and provides a scalable model for dengue-endemic regions worldwide.