Deep green diagnostics: Urban green space analysis using deep learning and drone images

Marco A. Moreno-Armendáriz, Hiram Calvo, Carlos A. Duchanoy, Anayantzin P. López-Juárez, Israel A. Vargas-Monroy, Miguel Santiago Suarez-Castañon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


© 2019 by the authors. 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.
Original languageAmerican English
JournalSensors (Switzerland)
StatePublished - 1 Dec 2019

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