## 2 Literature Review
### Trends in Urban Street Perception Research and the Innovativeness of This Study  
Over the preceding decades, research on urban perception has been continuously updated. The application of multi-source big data has brought new opportunities for research on urban perception [40]. Many studies have quantitatively evaluated the spatial perception of streets through indicators such as human activity patterns, built environment measures, and field visit results. Pedestrian counts and qualitative street studies have been used to explore the cultural differences and similarities in street vitality perceptions between street users in the United States and Turkey and to analyze the physical factors that contribute to increased street vitality [41]. Field visit results often need to rely on more extensive sample data. For example, [PERSON] et al. investigated the preferences and perceptions of street trees among 884 city residents in a metropolitan area in South Korea to explore strategies for improving the spatial quality of urban streets [42]. Meanwhile, advances in urban analysis tools provide new methods to describe, quantify, and present street quality perceptions. For example, [PERSON] et al. used a regression model to quantify the correlation between vitality and the factors affecting the built environment in two cities [43]. [PERSON] et al. used machine learning semantic segmentation, GIS, and semantic differential (SD) to obtain spatial data and perceptual evaluations of Qingdao coastal streets. They constructed a regression model with imageability, closure, human scale, transparency, and complexity as dependent variables [44]. A wide array of complex factors influence urban spatial perception. For example, [PERSON] et al. used random forest regression to reveal the non-linear effects of street canyon properties on human perception [45]. This research was principally conducted from the perspective of street-space characteristics. Additionally, a number of scholars have paid attention to the correlation between urban spatial perception and urban vitality [46]. Urban perception is also highly correlated with environmental exposures and urbanization factors. It was found that population density, impervious surface area, major roads, traffic air pollution, tree coverage, and NDVI resulted in statistically significant differences in levels of safety, liveliness, and perceived location beauty [47]. The study was exclusively focused on capturing the relationship between the factors associated with built-environment exposure and partial positive perception. Indeed, due to the complexity of factors influencing how an urban space is perceived, it is difficult to find all the influencing factors in a single study. For example, [PERSON] et al. investigated how the resulting objects--trees, grass, roads, bridges, houses, etc.--contribute to the curation of a certain experience [48]. Thus, we developed a comprehensive research paradigm to assess the impact of urban perception based on previous studies. In contrast to previous research, this study considers the macro and micro influences of the urban micro-level on the layout of various functional facilities. To summarize, existing research on big data technologies in urban street space has shown an upward trend. The development and application of semantic segmentation technology encouraged the algorithm's ongoing improvement, and its potent capability for image processing provided a strong framework for the execution of large-scale image data studies. The granular coverage of POI data in cities also provided a source of data for this study. Urban streetscape data sources, POI big data, and related technologies are all combined in this study. However, most of the existing studies still have some limitations.