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

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dc.contributor.author Trujillano Asato, Fedra Catherine
dc.contributor.author Jimenez Garay, Gabriel Alexandro
dc.contributor.author Alatrista-Salas, Hugo
dc.contributor.author Byrne, Isabel
dc.contributor.author Nunez-Del-Prado, Miguel
dc.contributor.author Chan, Kallista
dc.contributor.author Manrique, Edgar
dc.contributor.author Johnson, Emilia
dc.contributor.author Apollinaire, Nombre
dc.contributor.author Kouame Kouakou, Pierre
dc.contributor.author Oumbouke, Welbeck A.
dc.contributor.author Tiono, Alfred B.
dc.contributor.author Guelbeogo, Moussa W.
dc.contributor.author Lines, Jo
dc.contributor.author Carrasco Escobar, Gabriel
dc.contributor.author Fornace, Kimberly
dc.coverage.spatial Saponé, Burkina Faso
dc.coverage.spatial Bouaké, Costa de Marfil
dc.date.accessioned 2023-09-06T20:45:08Z
dc.date.available 2023-09-06T20:45:08Z
dc.date.issued 2023
dc.identifier.uri https://hdl.handle.net/20.500.12866/14068
dc.description.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. en_US
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartofseries Remote Sensing
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Malaria vector en_US
dc.subject Deep learning en_US
dc.subject Image classification en_US
dc.subject Drone images en_US
dc.subject Epidemiological control en_US
dc.subject.mesh Malaria
dc.subject.mesh Aprendizaje Profundo
dc.subject.mesh Clasificación
dc.subject.mesh Dispositivos Aéreos No Tripulados
dc.subject.mesh Monitoreo Epidemiológico
dc.title Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.3390/rs15112775
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#1.05.00
dc.relation.issn 2072-4292


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