A hierarchical deep learning model for predicting pedestrian-level urban winds

Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as sharp gradients and peak wind speeds. This study proposes a hierarchical approach for accurately predicting pedestrian-level urban winds, which adopts a two-stage predictor-refiner framework. In the first stage, a U-Net architecture generates a baseline prediction from urban geometry. In the second stage, a conditional Generative Adversarial Network (cGAN) refines this baseline by restoring the missing high-frequency content. The cGAN's generator incorporates a multi-scale architecture with stepwise kernel sizes, enabling simultaneous learning of global flow structures and fine-grained local features. Trained and validated on the UrbanTALES dataset with comprehensive urban configurations, the proposed hierarchical model significantly outperforms the baseline predictor. An ablation study against a single-stage cGAN (mapping geometry directly to LES fields) and a hierarchical CNN refiner (trained only with L1 loss) shows that both the two-stage design and the adversarial objective are necessary to achieve the reported accuracy gains. With a marked qualitative improvement in resolving high-speed wind jets and complex turbulent wakes as well as wind statistics, the results yield quantitative enhancement in prediction accuracy (e.g., RMSE reduced by 76 % for the training set and 60 % for the validation set). This work presents an effective and robust methodology for enhancing the prediction fidelity of surrogate models in urban planning, pedestrian comfort assessment, and wind safety analysis. The proposed model will be integrated into an interactive web platform as Feilian Version 2.