High-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania

Abstract

Larval source management is a key strategy to control the spread of Aedes-borne viral diseases including dengue, Zika, chikungunya, and yellow fever. However, locating potential larval habitats through traditional field methods is challenging and labor-intensive at scale. Here, we demonstrate a scalable, high-resolution drone imagery and supervised machine learning approach to map potential Aedes larval container habitats across Dar es Salaam, Tanzania, a dense urban environment with informal settlements. Local larval surveillance and existing literature confirm epidemiological relevance of buckets and jerry cans, tires, and water tanks as key potential habitats. Drone images revealed rooftop tires, a container type likely overlooked during ground surveillance. We trained a U-Net deep learning model on very-high-resolution drone imagery (3-5 cm resolution) which was manually annotated across 4.6 km2, and applied it to predict containers across 27.27 km2, spanning 20 neighborhoods. The model predicted over 135,000 containers with detection accuracies of 75% for water tanks, 72% for tires, and 54% for buckets. Bucket and tire densities were strongly and positively correlated with population density across neighborhoods, whereas water tank density was not, suggesting the distribution of these container types reflect distinct underlying drivers. This study reveals otherwise difficult-to-observe container types, highlights the abundance and spatial heterogeneity of potential Aedes larval habitats across Dar es Salaam, and demonstrates a scalable approach for improving detection of potential Aedes larval container habitats in a dense urban environment.