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arXiv · 2607.15527

Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection

Sodtavilan Odonchimed·Tsogt Enkhbayar·Oyunzul Munkhtamga·Munkhjargal Gochoo
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Abstract

Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and the development of anomaly detection models. However, the severe shortage of flood data in real-world environments, combined with the inherent distortions of fisheye lens images, which are used for urban surveillance, has made high-precision simulations challenging. To address this, we propose a new physical simulation system PhysFlood that leverages Diffusion Models to synthesize realistic floods from just a single image captured by a fisheye lens. Our system not only enables simulation from a single image, but also features the ability to freely control and generate diverse flood scenarios by manipulating physically meaningful variables, such as water levels. In our evaluation experiments, we conducted a qualitative human study and demonstrated that the simulation images generated by PhysFlood exhibit both acceptable realism and robustness.

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Sodtavilan Odonchimed, Tsogt Enkhbayar, Oyunzul Munkhtamga, Munkhjargal Gochoo. 2026-07-17. Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection. https://arxiv.org/abs/2607.15527

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