Quantifying the Urban Visual Perception of Chinese Traditional-Style Building with Street View Images

Applied Sciences

Liying Zhang¹  Yijin Chen²  Mingbo Wu³  Ci Song³  Sihui Guo³  Tao Pei³  Xi Wang³ 

[1] College of Information Science and Engineering, China University of Petroleum, Beijing 102249, China;School of Geosciences & Surveying Engineering, China University of Mining & Technology, Beijing 100083, China;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;

关键词: urban 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perception;    Chinese traditional-style building;    street view images;    view indicator;    deep learning;   

DOI  :  10.3390/app10175963

来源: DOAJ

【 摘 要 】

As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.

【 授权许可】

Unknown