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

Streetscape is an important part of the urban landscape, analysing and studying them can increase the understanding of the cities' infrastructure, which can lead 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 HRNet-OCR \cite{HRNet-OCR} is used to segment the Osaka City street view images and analyse the Green View Index (GVI) of Osaka City. Based on the GVI value, because of the limitations of ArcGIS software, we take advantage of adjacency matrix and Floyd algorithm is used to calculate Green View Index best path. Our analysis not only allows for the calculation of specific routes for the optimal GVI paths, but also allows for 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 maximised for a better life.