# Inpainting U-Net for seamless pedestrian-level wind prediction across urban morphologies

arXiv: https://arxiv.org/abs/2607.02560 · HTML: https://mlsanity.com/paper/2607.02560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 28 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks](https://mlsanity.com/paper/1608.00775/) (2016)
- [Introducing Hann windows for reducing edge-effects in patch-based image segmentation](https://mlsanity.com/paper/1910.07831/) (2019)
- [No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling](https://mlsanity.com/paper/2501.10814/) (2025)
- [Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture Synthesis](https://mlsanity.com/paper/2309.02340/) (2023)
- [From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions](https://mlsanity.com/paper/2507.06533/) (2025)
- [Configurable convolutional neural networks for real-time pedestrian-level wind prediction in urban environments](https://mlsanity.com/paper/2311.07985/) (2023)

## Abstract (arXiv)

Pedestrian-level wind prediction is essential for urban design and wind-comfort assessment, but high-fidelity simulations such as LES remain computationally expensive for rapid evaluation. This study develops a two-stage U-Net framework for efficient prediction of time-averaged pedestrian-level wind speed over realistic urban morphologies. The model is trained and evaluated using the UrbanTALES dataset, which contains realistic city configurations under different approaching wind directions. In the first stage, a baseline U-Net model (M1) predicts wind fields patch-by-patch from normalised building height and fetch information. This formulation allows application to urban domains of arbitrary size, but independent patch inference can introduce discontinuities at patch boundaries. To address this, a second U-Net model (M2) is introduced as an inpainting-based refinement model. M2 uses a larger contextual window containing the initial M1 prediction and local morphology to reduce discontinuities using neighbouring flow information. During full-field inference, M2 is applied iteratively using a Gauss-Seidel scheme until convergence. Results show that M1 captures the main spatial distribution of pedestrian-level wind speed and performs well in low- and moderate-velocity regions, although high-velocity peaks are less accurate. M2 substantially reduces patch-boundary artefacts and improves spatial coherence. Across unseen urban cases, the framework reproduces mean velocity and spatial variability reasonably well, while maximum velocities remain underestimated. Overall, the proposed framework provides an efficient and flexible surrogate model for high-resolution pedestrian-level wind prediction across realistic urban morphologies.