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

TL;DR

Flood-LDM introduces a latent diffusion model approach for rapid, accurate, and generalizable high-resolution flood mapping from coarse data, significantly improving real-time flood risk assessment capabilities.

Contribution

This paper presents a novel latent diffusion model that enhances flood map super-resolution, offering better speed, accuracy, and geographic generalizability over existing methods.

Findings

Reduces computational time for high-fidelity flood maps

Achieves comparable accuracy to traditional high-resolution models

Demonstrates superior transferability across different regions

Abstract

Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate high-resolution flood maps using numerical methods requiring fine-grid discretization; which are computationally intensive and impractical for real-time large-scale applications. While recent studies have applied convolutional neural networks for flood map super-resolution with good accuracy and speed, they suffer from limited generalizability to unseen areas. 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. Experimental results demonstrate that latent diffusion models substantially decrease the computational time required to produce high-fidelity flood maps without compromising on accuracy, 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. Our approach also incorporates physics-informed inputs, addressing the common limitation of black-box behavior in machine learning, thereby enhancing interpretability. Code is available at https://github.com/neosunhan/flood-diff.