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Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images
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 r...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928838/ https://www.ncbi.nlm.nih.gov/pubmed/31801291 http://dx.doi.org/10.3390/s19235287 |
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author | Moreno-Armendáriz, Marco A. Calvo, Hiram Duchanoy, Carlos A. López-Juárez, Anayantzin P. Vargas-Monroy, Israel A. Suarez-Castañon, Miguel Santiago |
author_facet | Moreno-Armendáriz, Marco A. Calvo, Hiram Duchanoy, Carlos A. López-Juárez, Anayantzin P. Vargas-Monroy, Israel A. Suarez-Castañon, Miguel Santiago |
author_sort | Moreno-Armendáriz, Marco A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6928838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69288382019-12-26 Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images Moreno-Armendáriz, Marco A. Calvo, Hiram Duchanoy, Carlos A. López-Juárez, Anayantzin P. Vargas-Monroy, Israel A. Suarez-Castañon, Miguel Santiago Sensors (Basel) Article 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. MDPI 2019-11-30 /pmc/articles/PMC6928838/ /pubmed/31801291 http://dx.doi.org/10.3390/s19235287 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moreno-Armendáriz, Marco A. Calvo, Hiram Duchanoy, Carlos A. López-Juárez, Anayantzin P. Vargas-Monroy, Israel A. Suarez-Castañon, Miguel Santiago Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title | Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title_full | Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title_fullStr | Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title_full_unstemmed | Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title_short | Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images |
title_sort | deep green diagnostics: urban green space analysis using deep learning and drone images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928838/ https://www.ncbi.nlm.nih.gov/pubmed/31801291 http://dx.doi.org/10.3390/s19235287 |
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