The project presented a workflow that couples panoramic images of a city with deep learning techniques for urban feature extraction to enable the estimation of outdoor thermal comfort in the absence of data-rich digital 3D models of cities. Through coupling semantic segmentation and depth mapping on street-view panoramic imagery, a comprehensive workflow is designed to combine these into a low-fidelity outdoor thermal comfort prediction.