Measuring Human Perception of Residential Built Environment through Street View Image and Deep Learning

Abstract

As an important part of the urban built environment, streets exploring the influence mechanism between the built environment and human perception. It is one of the issues in building healthy cities. In this study, the residential streets of Zhongshan Distict, Dalian were selected as the study site, including Mountain Low-rise Neighborhood, Old Mid-rise Neighborhood, and Modern High-rise Neighborhood. Meanwhile, spatial measurement and human perception perception evaluation of the street environment were based on Deep learning and street view image (SVI). The study used human perceptions as dependent variables, and physical features as the independent variables. Finally, two regression models of positive and negative perceptions were established to analyze the relationship between them. The results showed that in the three types of neighborhood, positive perception was mainly focused on Mountain Low-rise Neighborhood; Negative perception was mainly focused on Old Mid-rise Neighborhood. Greenness, Openness, Natural Landscape, Natural to artificial ratio of the horizontal interface, and Natural to artificial ratio of the vertical interface had a positive influence on positive perception. Pedestrian occurrence rate, Enclosure, and Vehicle Occurrence rate had a negative influence on negative emotive. Greenness was the physical feature that most affected human perception. This study provided a method for objectively evaluating the quality of the street built environment. It is important for promoting the quality of residential streets and public mental health.