2022
DOI: 10.3390/rs14020317
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Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

Abstract: Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping th… Show more

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Cited by 13 publications
(13 citation statements)
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References 34 publications
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“…The Technology-Assisted Digitizing (TAD) mapping approach using the RegionGrow tool was found to be significantly more accurate in extracting information about potential mosquito larval habitats from drone imagery compared to the supervised classification approach. Integrating the TAD approach into operational LSM programs is recommended due to its high accuracy results ( Hardy et al., 2022 ).…”
Section: Vector Control Methodsmentioning
confidence: 99%
“…The Technology-Assisted Digitizing (TAD) mapping approach using the RegionGrow tool was found to be significantly more accurate in extracting information about potential mosquito larval habitats from drone imagery compared to the supervised classification approach. Integrating the TAD approach into operational LSM programs is recommended due to its high accuracy results ( Hardy et al., 2022 ).…”
Section: Vector Control Methodsmentioning
confidence: 99%
“…This approach can include the following steps: Manual digitization: the simplest approach that relies on visual identification of key features; this has been shown to be effective for pinpointing malaria vector breeding sites in Tanzania [ 20 ]. Region-growing/technology-assisted digitizing: this approach builds on manual digitization by using automated approaches to classify similar neighbouring pixels [ 51 ]. Unsupervised classification: these methods group features within an image into different categories without the use of training data to define classes of interest.…”
Section: Integrating Drones Into Vector Surveillancementioning
confidence: 99%
“…Region-growing/technology-assisted digitizing: this approach builds on manual digitization by using automated approaches to classify similar neighbouring pixels [ 51 ].…”
Section: Integrating Drones Into Vector Surveillancementioning
confidence: 99%
“…Remote sensing is a powerful tool for studying the larval habitats of mosquitoes, particularly those that transmit diseases such as malaria [ 14 , 15 ]. Several studies have highlighted the increased effectiveness of integrating remote sensing into the control systems of these vector-borne diseases [ 16 ].…”
Section: Introductionmentioning
confidence: 99%