Analyzing Green View Index and Green View Index best path using Google Street View and deep learning
This work addresses urban planning and resident well-being by providing tools for optimizing green space usage, though it is incremental as it applies existing techniques to a new dataset.
The authors tackled the problem of analyzing street-level greenery in Osaka City by using Google Street View images and a semantic segmentation model to compute the Green View Index (GVI), and they developed a method to calculate the best path for maximizing greenery exposure using an adjacency matrix and Floyd-Warshall Algorithm, achieving visualization and integration of neighborhood greenery data.
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.