Quantification through deep learning of sky view factor and greenery on urban streets during hot and cool seasons

The urban heat island effect has gained attention worldwide. Built environment characteristics such as sky view factor (SVF) and green view index (GVI) can affect urban thermal environments and pedestrians' thermal comfort. With recent technological advances, Google Street View (GSV) can be used to rapidly obtain panoramic street-view images with high reliability, enabling convenient and low-cost environmental assessment of urban settings. In addition, deep learning technology for quantifying the characteristics of urban environments has advanced considerably. This study sought to (1) determine the consistency between deep learning and manual classification of urban environment characteristics and (2) investigate the effects of street-level SVF and GVI on thermal comfort, especially the differences in their effects during hot and cool seasons. The study was conducted in the West District of Taichung City, and GSV was used to capture images from which SVF and GVI were calculated. A total of 50 sample locations were selected for an onsite questionnaire and thermal comfort was measured to determine the effects of SVF and GVI. The results indicated deep learning and manual classifications of SVF and GVI to be highly correlated. With regard to effects, SVF had a significant positive effect on physio-logical equivalent temperature and thermal sensation votes. GVI also had a significant positive effect on phys-iological equivalent temperature, but no effect on thermal sensation votes. Thus, reducing SVF and implementing greening projects may improve thermal comfort of pedestrians on the streets. These results offer implications for future urban planning and large-scale urban thermal environment assessments.