Green View Index Analysis and Optimal Green View Index Path Based on Street View and Deep Learning

• URL: http://arxiv.org/abs/2104.12627v1
• Date: Mon, 26 Apr 2021 14:53:21 GMT
• Title: Green View Index Analysis and Optimal Green View Index Path Based on Street View and Deep Learning
• Authors: Anqi Hu, Jiahao Zhang and Hiroyuki Kaga
• Abstract summary: In this paper, we used Google API to obtain street view images of Osaka City. PSPNet is used to segment the Osaka City street view images and analyse the Green View Index (GVI) data of Osaka area. Three methods, namely corridor analysis, geometric network and a combination of them, were then used to calculate the optimal GVI paths.
• Score: 2.8682942808330703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streetscapes are 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 API to obtain street view images of Osaka City. The semantic segmentation model PSPNet is used to segment the Osaka City street view images and analyse the Green View Index (GVI) data of Osaka area. Based on the GVI data, three methods, namely corridor analysis, geometric network and a combination of them, were then used to calculate the optimal GVI paths in Osaka City. The corridor analysis and geometric network methods allow for a more detailed delineation of the optimal GVI path from general areas to specific routes. Our analysis not only allows for the calculation of specific routes for the optimal GVI paths, but also allows for the visualisation and integration of neighbourhood landscape data. 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.

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