ArticleRemote sensing2023
Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance.
Article in Remote sensing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- Non-linear age dynamics of malaria infection and fine-scale environmental exposure in rural Uganda.BMC medicine · 2026Article
- Deep learning identifies water bodies from low-cost drone images for mosquito larval habitat mapping.Scientific reports · 2026Article
- High-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania.PLoS neglected tropical diseases · 2026Article
- EnhancingRemote sensing applications : society and environment · 2026Article
- New frontiers for unmanned aerial vehicles in planetary health research.BMC global and public health · 2026Review
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Article
- A large-scale mosquito larviciding in Tanga Region, Tanzania, reduced mosquito densities to varying degrees across malaria transmission risk strata.Scientific reports · 2025Article
- Monitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control.Malaria journal · 2025Article
- Considerations for first field trials of low-threshold gene drive for malaria vector control.Malaria journal · 2024Review
- Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.International journal of health geographics · 2024Article
- Risks of Drone Use in Light of Literature Studies.Sensors (Basel, Switzerland) · 2024Review
- Innovative strategies and challenges mosquito-borne disease control amidst climate change.Frontiers in microbiology · 2024Review
- Cost and quality of operational larviciding using drones and smartphone technology.Malaria journal · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
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 Côte 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.