Research On The Evaluation And Improvement Of Street View Visual Comfort Based On Big Data

The accelerated pace of life in modern cities,the increasing pressure of social competition,the sedentary and sedentary lifestyles that lack physical activity,especially the continuous reduction of green space and natural environment caused by the high-density development of cities,are important factors that cause various mental diseases and chronic diseases.These health diseases that perplex modern urban residents are gradually expanding.If they are not effectively prevented and controlled,they will seriously threaten human survival in the near future.Modern urban residents urgently need living space and places to relieve mental pressure and improve physical activity level.Urban residents have a positive attitude towards health recovery,and their demand for recovery is urgent.The rehabilitation needs of urban residents can be met through the provision of an effective environment.The restorative environment can help residents relieve mental stress,eliminate bad emotions,reduce mental fatigue and recover attention,promote physical and mental health,and meet the recovery needs of residents.Based on the theoretical expansion of environmental comfort and the application practice of street scene big data,the street scene of different lots of Hong qi Street in Shijiazhuang City is discussed in terms of comfort perception,comfort factors and the difference in comfort distribution between cities.The street is an important component of the urban skeleton and texture,and the urban link connecting different functional spaces in the city;The green space next to the street is a place of daily social life and has social attributes.When the increasingly serious problems of traffic noise and pedestrian environment safety appear,gradually separating crowd behavior activities from street space,the traffic function of the street gradually surpasses other functions as a place for living,living and activities,and the solution of these problems requires the classification and analysis of different functional influencing elements.In the past era,when big data was not popularized enough,we were able to collect information in general terms through simple technical means such as questionnaire survey methods,but questionnaire surveys have limitations,only the results of the survey from the subjective perspective of the population can be obtained,and the lack of data support will make it difficult to make accurate planning for subsequent construction.With the development of society and the country,big data,especially in terms of landscape,relevant information has become increasingly mature and perfect,in the context of big data in the development of modern technology,we can obtain accurate and complete street view image collection without leaving home,using image semantic segmentation technology can accurately obtain and divide picture information and various elements in different street view pictures,through K-means algorithm can be clustered analysis of street scene element composition characteristics.With accurate data support,coupled with the collection of subjective evaluation of Street View comfort users through online questionnaires,and the correlation analysis and data comparison of the two at the same time,we can successfully establish the connection between people’s perception of Street View comfort and objective street environment elements,and distinguish street scene elements that have positive and negative effects on comfort.By mapping the comfort evaluation results in the urban space,the distribution and differences in comfort between different areas of the city can be clearly reflected.Therefore,the application of landscape big data is the general trend,this thesis will conduct a detailed study on the evaluation and improvement of streetscape comfort based on big data,and take Shijiazhuang Hong qi Street as an example to test,on the basis of the classification of streetscape visual features,summarize the planning and design suggestions for the improvement of comfort of different types of streets,and explore new possibilities for further forming a more refined and multidimensional streetscape classification standard in the future,and the research results can provide reference for the future renovation and upgrading of old streetscapes and the planning and design of new streetscapes.