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

Researchers propose PhysFlood, a physics-aware masked diffusion-based simulation system that generates realistic flood images from a single fisheye lens photo, addressing the shortage of flood data for urban disaster detection. The system allows control of variables like water level to create diverse flood scenarios, and a qualitative human study confirmed the generated images show acceptable realism and robustness.

arXiv:2607.15527v1 Announce Type: new 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.