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Urban planners and policy makers face the challenge of creating livable andenjoyable cities for larger populations in much denser urban conditions. Whilethe urban microclimate holds a key role in defining the quality of urban spacestoday and in the future, the integration of wind microclimate assessment inearly urban design and planning processes remains a challenge due to thecomplexity and high computational expense of computational fluid dynamics (CFD)simulations. This work develops a data-driven workflow for real-time pedestrianwind comfort estimation in complex urban environments which may enabledesigners, policy makers and city residents to make informed decisions aboutmobility, health, and energy choices. We use a conditional generativeadversarial network (cGAN) architecture to reduce the computational computationwhile maintaining high confidence levels and interpretability, adequaterepresentation of urban complexity, and suitability for pedestrian comfortestimation. We demonstrate high quality wind field approximations whilereducing computation time from days to seconds.

Pedestrian Wind Factor Estimation in Complex Urban Environments