Analyzing Green View Index and Green View Index best path using Google Street View and deep learning

TL;DR

This study utilizes Google Street View images and deep learning to analyze street-level greenery in Osaka, Japan, and calculates optimal paths to maximize urban greenery exposure, aiding urban planning and resident well-being.

Contribution

It introduces a novel method combining semantic segmentation and graph algorithms to identify the Green View Index best path, overcoming limitations of existing GIS tools.

Findings

Successfully calculated GVI best paths in Osaka

Visualized neighborhood greenery and its distribution

Provided open dataset and code for further research

Abstract

As an important part of urban landscape research, analyzing and studying street-level greenery can increase the understanding of a city's greenery, contributing to better urban living environment planning and design. Planning the best path of urban greenery is a means to effectively maximize the use of urban greenery, which plays a positive role in the physical and mental health of urban residents and the path planning of visitors. In this paper, we used Google Street View (GSV) to obtain street view images of Osaka City. The semantic segmentation model is adopted to segment the street view images and analyze the Green View Index (GVI) of Osaka City. Based on the GVI, we take advantage of the adjacency matrix and Floyd-Warshall Algorithm to calculate Green View Index best path, solving the limitations of ArcGIS software. Our analysis not only allows the calculation of specific routes for the GVI best paths but also realizes the visualization and integration of neighborhood urban greenery. By summarizing all the data, we can conduct an intuitive feeling and objective analysis of the street-level greenery in the research area. Based on this, such as urban residents and visitors can maximize the available natural resources for a better life. The dataset and code are available at https://github.com/Jackieam/GVI-Best-Path.