Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Moreno-Armendariz MA

PROVIDER: S-EPMC6928838 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Moreno-Armendáriz Marco A MA Calvo Hiram H Duchanoy Carlos A CA López-Juárez Anayantzin P AP Vargas-Monroy Israel A IA Suarez-Castañon Miguel Santiago MS

Sensors (Basel, Switzerland) 20191130 23

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 supervis ...[more]

PMID: 31801291

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Project description:BackgroundExposure to a diverse environmental microbiome is thought to play an important role in "educating" the immune system and facilitating competitive exclusion of pathogens to maintain human health. Vegetation and soil are key sources of airborne microbiota--the aerobiome. A limited number of studies have attempted to characterize the dynamics of near surface green space aerobiomes, and no studies to date have investigated these dynamics from a vertical perspective. Vertical stratification in the aerobiome could have important implications for public health and for the design, engineering, and management of urban green spaces.ObjectivesThe primary objectives of this study were to: a) assess whether significant vertical stratification in bacterial species richness and evenness (alpha diversity) of the aerobiome occurred in a parkland habitat in Adelaide, South Australia; b) assess whether significant compositional differences (beta diversity) between sampling heights occurred; and c) to preliminarily assess whether there were significant altitudinal differences in potentially pathogenic and beneficial bacterial taxa.MethodsWe combined an innovative columnar sampling method at soil level, 0.0, 0.5, 1.0, and 2.0m, using passive petri dish sampling to collect airborne bacteria. We used a geographic information system (GIS) to select study sites, and we used high-throughput sequencing of the bacterial 16S rRNA gene to assess whether significant vertical stratification of the aerobiome occurred.ResultsOur results provide evidence of vertical stratification in both alpha and beta (compositional) diversity of airborne bacterial communities, with diversity decreasing roughly with height. We also found significant vertical stratification in potentially pathogenic and beneficial bacterial taxa.DiscussionAlthough additional research is needed, our preliminary findings point to potentially different exposure attributes that may be contingent on human height and activity type. Our results lay the foundations for further research into the vertical characteristics of urban green space aerobiomes and their implications for public health and urban planning. https://doi.org/10.1289/EHP7807.

Dataset Information

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

Publications

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

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