Publication: Large scale detailed mapping of dengue vector breeding sites using street view images
Issued Date
2019-07-01
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ISSN
19352735
19352727
19352727
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2-s2.0-85071348877
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Mahidol University
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SCOPUS
Bibliographic Citation
PLoS Neglected Tropical Diseases. Vol.13, No.7 (2019)
Suggested Citation
Peter Haddawy, Poom Wettayakorn, Boonpakorn Nonthaleerak, Myat Su Yin, Anuwat Wiratsudakul, Johannes Schöning, Yongjua Laosiritaworn, Klestia Balla, Sirinut Euaungkanakul, Papichaya Quengdaeng, Kittipop Choknitipakin, Siripong Traivijitkhun, Benyarut Erawan, Thansuda Kraisang Large scale detailed mapping of dengue vector breeding sites using street view images. PLoS Neglected Tropical Diseases. Vol.13, No.7 (2019). doi:10.1371/journal.pntd.0007555 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/51567
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Title
Large scale detailed mapping of dengue vector breeding sites using street view images
Abstract
© 2019 Haddawy et al. Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale.