Climate change exacerbates outdoor thermal conditions globally and locally, particularly in densely populated urban areas. One key metric representing these conditions is the sky view factor (SVF), often overlooked in district-scale outdoor thermal comfort enhancement. Our study unravels the interplay between SVF, thermal environments, and pedestrian thermal comfort at the district scale. We conducted a mobile survey in Singapore’s one-north district, measuring thermal environments and capturing panoramic imagery, and employed deep learning for SVF estimation. We developed a multiple regression model, estimating thermal comfort from SVF and meteorological variables. A 0.21 SVF reduction corresponds to a 1 °C (33.8 °F) thermal perception decrease. This model empowers urban planners and designers to wield SVF adjustments to optimize outdoor environments and foster climate-resilient and livable urban environments.
