Publication: Large scale detailed mapping of dengue vector breeding sites using street view images
Issued Date
2019
Resource Type
Language
eng
Rights
Mahidol University
Rights Holder(s)
PLOS ONE
Bibliographic Citation
PLOS Neglected Tropical Diseases. Vol. 13, No.7, e0007555
Suggested Citation
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 Large scale detailed mapping of dengue vector breeding sites using street view images. PLOS Neglected Tropical Diseases. Vol. 13, No.7, e0007555. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/47883
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Large scale detailed mapping of dengue vector breeding sites using street view images
Editor(s)
Other Contributor(s)
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.