  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.
