Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping

Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate accurate high-resolution flood maps using numerical methods requiring fine-grid discretization; which are computationally intensive for large areas and impractical for real-time applications. While recent studies have applied convolutional neural networks for flood map super-resolution with good accuracy and speed, these models suffer from limited generalizability when applied to unseen areas or novel flood scenarios. In this paper, we propose a novel approach that leverages latent diffusion models to perform super-resolution on coarse-grid flood maps, with the objective of achieving the accuracy of fine-grid flood maps while significantly reducing inference time. Our experimental results demonstrate that latent diffusion models can substantially decrease the computational time required to produce high-fidelity flood maps without compromising on accuracy, thus enabling their use in real-time flood risk management. Moreover, diffusion models exhibit superior generalizability across different physical locations, with transfer learning further accelerating adaptation to new geographic regions with limited efforts. Finally, by incorporating physics-informed inputs into the model, our approach addresses the common limitation of black-box behavior in machine learning, thereby enhancing interpretability. Code will be made publicly available upon acceptance.