## 2 Related Work  
### Previous Research on the Urban Built Environment  
In early studies, the descriptions of the built environment were mainly based on site or community audits, which were time-consuming, labor-intensive, and could not be applied at large spatial scales [17; 18; 19]. Some scholars use GIS tools to describe the urban environment [20]. With the development of network mapping technology, urban streetscape images along road networks have been produced, giving rise to new ideas for the study of urban built-up area environments. [PERSON] et al. [13] used street view images to portray the urban visual environment, mining street view picture information with the help of the deep learning semantic segmentation model, creatively proposing scene semantic trees and scene expression vectors, which help to qualitatively understand and quantitatively analyze the cityscape. Through their research, [PERSON] et al. [20] demonstrated through research that Google Street View images can be used to evaluate the audit of the neighborhood environment, which has the characteristics of high efficiency and low cost. [PERSON] et al. [21] used Google Street View images to construct road environment features instead of collecting data in the field to evaluate the environmental features, and the results of the study proved that using street view images to evaluate the road environment is a reliable method. [PERSON] et al. [22] assessed bicycling- and walking-related built environment features through a field survey and a Google Street View image analysis of 48 street segments in Oakland. The results show that the two methods demonstrated consistency across most of the assessed features, suggesting that Google Street View imagery can be a useful supplement to field surveys for assessing urban environmental features. [PERSON] et al. [23] showed that the average person can clearly interpret 84% of the features in the environmental factors represented by Google Street View; and [PERSON] [24] and others have shown that Street View images can provide some reliable indicators for evaluating local food environments and recreational facilities.  
By evaluating the built environment of roadway segments and intersections, it was found that factors such as pedestrian and vehicular traffic flow, roadway width, and lighting have a definite impact on drivers [25], which can increase the probability of traffic accidents. In addition, factors such as medians and trees can have an impact on pedestrians on the roadway [26], which can lead to crashes between pedestrians and motor vehicles. [PERSON] et al. [27] used street view imagery to examine associations between pedestrians and roadway infrastructure features and crash severity, which provided recommendations for pedestrian safety. [PERSON] et al. [28] used Google Street View photos to assess the impacts of real-world car accidents and found that car drivers may have a greater likelihood of being injured. [PERSON] et al. [29] then analyzed the built environment characteristics of traffic black spot areas and found that most of the areas with traffic black spots had bad roads, while in areas with fewer traffic accidents, the roads were wide and clean, and both motor vehicle and pedestrian densities were lower. In addition, [PERSON] et al. [30] found that there is a connection between the built environments of neighboring road intersections based on the urban built environment using the graph convolutional neural network (GCNN) model, which can affect the probability of traffic congestion at the intersection. All of these studies have laid the theoretical and technical foundation for this paper to identify traffic black spots based on street view image data.