Quantifying the Urban Visual Perception of Chinese Traditional-Style Building with Street View Images
Year: 2020
Type: article
Abstract: 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.
Source: Applied Sciences
DOI: 10.3390/app10175963
Institutions: China University of Petroleum, Beijing, China University of Mining and Technology, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System, China University of Mining and Technology - Beijing, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, University of Chinese Academy of Sciences
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,901
OpenAlex ID: W3081903687
