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

Marco A Moreno-Armendáriz¹, Hiram Calvo¹, Carlos A Duchanoy^1,2

¹Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de México 07738, Mexico.

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|December 6, 2019

Summary

This study introduces a deep learning tool to map urban land health and contamination, aiding public health initiatives in cities. The open-source software helps identify areas needing attention for improved urban living.

Keywords:

biomass analysis deep learning (for social good)remote sensing

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Area of Science:

Environmental Science
Urban Planning
Public Health

Background:

Increasing global urban populations necessitate monitoring urban environmental health.
Urban green space quality and quantity are linked to population health.
Supervising large urban land areas for contamination is a significant challenge.

Purpose of the Study:

To develop a deep learning solution for assessing urban land health and contamination.
To create software for generating updated maps of land health and contamination in cities.
To support public health institutions in urban environmental management.

Main Methods:

Utilizing deep learning algorithms for land health assessment.
Developing software for automated mapping of urban land conditions.
Analyzing land data to identify potential contamination.

Main Results:

A functional deep learning-based software for urban land health assessment.
Experimental data demonstrating the software's capability in identifying land health issues.
Freely available open-source code and experimental data.

Conclusions:

Deep learning offers a viable solution for monitoring urban land health at scale.
The developed software can significantly aid public health efforts in large cities.
Open access to code and data promotes further research and application in urban environmental monitoring.