Analysis Of Green Vision Rate In Beijing Five Rings Based On Street View Image

This paper uses the streetscape data of the main areas of the Wuhuan City District of Beijing collected by the Information Center of the Beijing Municipal Bureau of Landscaping and Greening,selects the streetscape photos of different years,uses the techniques of machine learning and deep learning to extract the elements in the pictures,and filters them according to the method of classification and aggregation.The green element is extracted,and the green field rate of the study area is calculated through the analysis model operation.Through the techniques and methods of big data analysis,the quantitative evaluation of large-scale and fine-scale regional landscape greening can be realized.Based on the calculation results of green viewing rate at each point,the greening of the research area can be evaluated horizontally.By comparing the results of green vision calculations in different seasons in the same season,it is possible to longitudinally analyze the change of green vision rate in the study area and try to explain the reasons for the change,and propose the optimization proposal of Beijing green vision rate improvement.This paper analyzes the current situation of Beijing's green viewing rate based on the summer green rate of 2017,including the overall distribution characteristics of Beijing's green viewing rate,the green viewing rate of each loop,and the green area rate of the core area.And the green summer rate change analysis in the summer of 2017,according to the road classification basis,the green line rate change analysis was carried out on the loop,the main secondary trunk road and the branch road,and some streets were selected in the winter of 2014 and the winter of 2015.The rate is supplemented by a change analysis.This article provides experience for a wide range of analysis of urban green viewing rates.According to the research conclusions,suggestions for the improvement of green rate and three-dimensional greening in Beijing are proposed to further promote the fine construction of landscaping.