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Open AccessArticle

Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images

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Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de México 07738, Mexico
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Cátedra CONACyT, Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de México 07738, Mexico
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Escuela Superior de Cómputo, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Col. Lindavista, Ciudad de México 07738, Mexico
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Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5287; https://doi.org/10.3390/s19235287
Received: 26 September 2019 / Revised: 15 November 2019 / Accepted: 28 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Intelligent Sensors for Smart City)
Nowadays, more than half of the world’s population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly urbanized locations. In particular, some of the recent work has focused on relating people’s health to the quality and quantity of urban green areas. In this context, and considering the huge amount of land area in large cities that must be supervised, our work seeks to develop a deep learning-based solution capable of determining the level of health of the land and to assess whether it is contaminated. The main purpose is to provide health institutions with software capable of creating updated maps that indicate where these phenomena are presented, as this information could be very useful to guide public health goals in large cities. Our software is released as open source code, and the data used for the experiments presented in this paper are also freely available. View Full-Text
Keywords: deep learning (for social good); remote sensing; biomass analysis deep learning (for social good); remote sensing; biomass analysis
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Moreno-Armendáriz, M.A.; Calvo, H.; Duchanoy, C.A.; López-Juárez, A.P.; Vargas-Monroy, I.A.; Suarez-Castañon, M.S. Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images. Sensors 2019, 19, 5287.

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