Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Cote d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Automated summary

Disease control programs rely on identifying mosquito breeding sites to target interventions and identify risk factors. The use of drone imagery and deep learning methods in this study successfully identified land cover types associated with vector breeding sites in malaria-endemic regions. The developed classifier achieved high accuracy in identifying vegetated and non-vegetated water bodies, as well as other land cover types such as tillage, crops, buildings, and roads. This study provides a framework for utilizing deep learning approaches in identifying vector breeding sites and highlights the importance of evaluating how these results can be used in disease control programs.