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Title: Large scale detailed mapping of dengue vector breeding sites using street view images
Authors: Haddawy, Peter
Poom Wettayakorn
Boonpakorn Nonthaleerak
Myat Su Yin
Anuwat Wiratsudakul
Johannes Scho¨ning
Yongjua Laosiritaworn
Balla, Klestia
Sirinut Euaungkanakul
Papichaya Quengdaeng
Kittipop Choknitipakin
Siripong Traivijitkhun
Erawan, Benyarut
Thansuda Kraisang
Reiner, Robert C.
Mahidol University. Faculty of Information Communication Technology
University of Bremen, Germany. Bremen Spatial Cognition Center
Mahidol University. Faculty of Veterinary Science
Ministry of Public Health
University of Camerino. Computer Science Department, School of Science and Technology
Keywords: Dengue;Aedes aegypti;Mosquitoes;Chikungunya;Google Street View;Dengue vector breeding
Issue Date: 2019
Citation: PLOS Neglected Tropical Diseases. Vol. 13, No.7, e0007555
Abstract: 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.
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