ArticleParasites & vectors2025
Comparison of unmanned aerial vehicle imaging to ground truth walkthroughs for identifying and classifying trash sites serving as potential Aedes aegypti breeding grounds.
Article in Parasites & vectors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- EnhancingRemote sensing applications : society and environment · 2026Article
- Drone-based composite risk mapping reveals vegetation-shade interaction and housing typology as key determinants of Aedes habitat risk.Scientific reports · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundTrash piles and abandoned tires that are exposed to the elements collect water and create productive breeding grounds for Aedes aegypti mosquitoes, the primary vector for multiple arboviruses. Unmanned aerial vehicle (UAV) imaging provides a novel approach to efficiently and accurately mapping trash, which could facilitate improved prediction of Ae. aegypti habitat and consequent arbovirus transmission. This study evaluates the efficacy of trash identification by UAV imaging analysis compared with the standard practice of walking through a community to count and classify trash piles.
methodsWe conducted UAV flights and four types of walkthrough trash surveys in the city of Kisumu and town of Ukunda in western and coastal Kenya, respectively. Trash was classified on the basis of a scheme previously developed to identify high and low risk Aedes aegypti breeding sites. We then compared trash detection between the UAV images and walkthrough surveys.
resultsAcross all walkthrough methods, UAV image analysis captured 1.8-fold to 4.4-fold more trash than the walkthrough method alone. Ground truth validation of UAV-identified trash showed that 94% of the labeled trash sites were correctly identified with regards to both location and trash classification. In addition, 98% of the visible trash mimics documented during walkthroughs were correctly avoided during UAV image analysis. We identified advantages and limitations to using UAV imaging to identify trash piles. While UAV imaging did miss trash underneath vegetation or buildings and did not show the exact composition of trash piles, this method was efficient, enabled detailed quantitative trash data, and granted access to areas that were not easily accessible by walking.
conclusionsUAVs provide a promising method of trash mapping and classification, which can improve research evaluating trash as a risk factor for infectious diseases or aiming to decrease community trash exposure.
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