Comparison of Vegetation Spectral Indices Based on UAV Data : Land Cover Identification Near Small Water Bodies

Komárková, Jitka; Jech, J.; Sedlák, Pavel

doi:10.23919/cisti49556.2020.9140899

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…For example, Komarkova et al [31] calculated eight vegetation indices calculated from 79 red, green, and blue (RGB) UAV imagery with a 1.7 cm per pixel resolution; indices 80 were identified, which allowed the identification of vegetated and clear water surfaces.…”

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confidence: 99%

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

Trujillano¹,

Jiménez²,

Alatrista-Salas³

et al. 2023

Preprint

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Disease control programs need to identify breeding sites of mosquitoes which transmit malaria and other diseases to target interventions and identify environmental risk factors. Increasing availability of very high resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, we identified land cover types associated with malaria vector breeding sites in West Africa. 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 classify these habitat types from very high resolution natural color imagery. Analysis methods achieved a dice coefficient ranging between 0.68 and 0.88 for different vector habitat types; however, this classifier consistently identified the presence of specific habitat types of interest. This 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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mentioning

confidence: 99%

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

Trujillano¹,

Jiménez²,

Alatrista-Salas³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

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

et al. 2023

View full text Add to dashboard Cite

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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Comparison of Vegetation Spectral Indices Based on UAV Data : Land Cover Identification Near Small Water Bodies

Cited by 2 publications

References 17 publications

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

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

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

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