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

As an important part of the urban landscape research, analysing and studying streetscape can increase the understanding of the cities' infrastructure, which contributes to better planning and design of the urban living environment. In this paper, we used Google Street View to obtain street view images of Osaka City. The semantic segmentation model is adopted to segment the Osaka City street view images and analyse the Green View Index (GVI). Based on the GVI value, we take advantage of adjacency matrix and Floyd algorithm is used 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 realize the visualization and integration of neighbourhood landscape. By summarising all the data, a more specific and objective analysis of the landscape in the study area can be carried out, and based on this, the available natural resources can be maximized for a better life.