Real-time rooftop wind-speed distribution is essential for safe operation of drones or urban air mobilities, wind control systems, and rooftop utilization. However, rooftop flows exhibit strong nonlinearity, separation, and cross-direction variability, which challenge flow field reconstruction from sparse sensors. This study develops a learning-from-observation framework using wind-tunnel experiment data obtained by Particle Image Velocimetry (PIV) and benchmarks Kriging interpolation against three deep learning models—UNet, Vision Transformer Autoencoder (ViTAE), and Conditional Wasserstein GAN (CWGAN). We evaluate two training strategies—single wind-direction training (SDT) and mixed wind-direction training (MDT)—across sensor densities of 5–30, test robustness under 1-grid sensor position perturbations, and optimize sensor placement via Proper Orthogonal Decomposition with QR decomposition. Results indicate the potential of deep learning methods to reconstruct rooftop wind fields from sparse sensor data. Compared with traditional Kriging interpolation, the deep learning models achieved improvements of up to 32.7% in SSIM, 24.2% in FAC2, and 27.8% in NMSE. In addition, training with mixed wind directions was found to be essential for achieving better model performance, resulting in further improvements of up to 173.7% in SSIM, 16.7% in FAC2, and 98.3% in MG compared with single-direction training. Furthermore, the comparison among methods indicates that sensor configuration, optimization, and training strategy should be jointly considered to ensure reliable deployment. For example, QR-based optimization enhances robustness by up to 27.8% under sensor perturbations, although with metric-dependent performance trade-offs. By training on experimental rather than simulated data, the proposed framework also offers practical guidance for method selection and sensor placement across different scenarios. The source code for this project is publicly available as open source on Github ( https://github.com/Yng314/windreconstruction ). • Comparing deterministic and generative learning methods against Kriging interpolation. • Evaluating methods and strategies under real-world constraints using experimental data. • Investigating a sensor position optimization method and its effectiveness. • Providing method selection advice under various application scenarios.
Zhou et al. (Sun,) studied this question.