Research on the Coupling Relationship Between Street Built Environment and Thermal Comfort Based on Deep Learning of Street View Images: A Case Study of Chaowai Block in Beijing

Against the background of global climate change receiving widespread attention, local microclimate environments have become a key focus of climate change research, which is of great significance for improving the quality of urban living environments. This study explored the quantitative coupling relationship between the built environment and the thermal comfort of complex streets. Outward blocks in Beijing were used as an example. By applying deep learning to street view images of an arterial road, we built three levels of road environmental elements for a quantitative analysis, simulated the block thermal comfort, numerically extracted the built environment factor, and derived a regression equation of the thermal comfort. The research results show that the UTCI value range of the Chaowai Block is between 28.15 °C and 47.11 °C, corresponding to human thermal sensations from slightly warm to very hot. The green rate, expressways, road width, spacious surroundings, sky, traffic, and ancillary facilities significantly affected the thermal comfort. Through the regression equation results, it can be found that the thermal comfort of different levels of roads is affected by multiple street built environment factors, and these influencing factors show differences in various levels of roads. Based on the results of the regression equation, corresponding optimization strategies were proposed to improve the thermal environment of urban streets and enhance the thermal comfort of pedestrians.