ater tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue contro

Author(s)

Cunha, Higor Souza ^[1] ; Sclauser, Brenda Santana ^[1] ; Wildemberg, Pedro Fonseca ^[2] ; Militao Fernandes, Eduardo Augusto ^[2] ; dos Santos, Jefersson Alex ^[2] ; Lage, Mariana de Oliveira ^[3] ; Lorenz, Camila ^[4] ; Barbosa, Gerson Laurindo ^[5] ; Quintanilha, Jose Alberto ^[6] ; Chiaravalloti-Neto, Francisco ^[4]

Affiliation

^[1] Univ Sao Paulo, Polytech Sch, Dept Elect Engn, Sao Paulo - Brazil

^[2] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG - Brazil

^[3] Univ Sao Paulo, Inst Energy & Environm, Environm Sci Grad Program PROCAM, Sao Paulo - Brazil

^[4] Univ Sao Paulo, Fac Publ Hlth, Dept Epidemiol, Sao Paulo - Brazil

^[5] State Dept Hlth, Endem Control Superintendence, Sao Paulo - Brazil

^[6] Univ Sao Paulo, Inst Energy & Environm, Sci Div Environm Management Sci & Technol, Sao Paulo - Brazil

• Document type: Journal article
• Source: PLoS One; v. 16, n. 12 DEC 9 2021.
• Web of Science Citations: 0

Abstract

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, SAo Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health. (AU)

• FAPESP's process: 15/06687-3 - Evaluation of importance from the strategic places in dispersion of the vector Aedes aegypti, and the use of premise condition index and remote sensing images for risk areas identification for Aedes aegypti
• Grantee: Gerson Laurindo Barbosa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/01596-8 - Use of remote sensing and artificial intelligence to predict high risk areas for Aedes aegypti and Arbovirus infestation
• Grantee: Francisco Chiaravalloti Neto
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/10297-1 - Identification of risk areas for arboviruses using traps for adults of Aedes aegypti and Aedes albopictus and remote sensing images
• Grantee: Camila Lorenz
• Support Opportunities: Scholarships in Brazil - Post-Doctoral