Abstract Accurately estimating low‐altitude wind speed (WS) is a critical and challenging task, with significant implications for urban meteorology and pollution dispersion modeling. This study developed a novel three‐dimensional Physics‐Integrated Swin‐Transformer (3D‐PST) deep learning model to estimate high‐resolution WS in the urban boundary layer. Comprehensive evaluations demonstrate that the 3D‐PST model outperforms existing methods across all key metrics, achieving state‐of‐the‐art results. Notably, the Root Mean Square Error reaches 1.10 m/s with a spatial resolution of 130 layers, while the correlation coefficient (R) is as high as 0.93, indicating a strong predictive capability. Furthermore, ablation studies reveal that key components like dynamic physical variables improve accuracy by 10%, and the convolution patch merging yields 12% improvement by more effectively capturing multi‐scale turbulence features. These results highlight the adaptability and robustness of the 3D‐PST model, showing its potential as a powerful tool for urban meteorological monitoring and supporting low‐altitude economic development.

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