Deep Learning for Remote Sensing to Improve Flood Inundation Mapping

2026-06-01

AI Summary

This study addresses the challenge of cloud occlusion in optical remote sensing imagery during flood events, which severely hinders accurate reconstruction of inundation dynamics using conventional cloud removal methods. To overcome this limitation, the work proposes a novel cloud removal framework based on denoising diffusion probabilistic models, introducing for the first time the Masked Diffusion Transformer to flood-related remote sensing tasks. By leveraging self-attention mechanisms and masked token modeling, the method explicitly reconstructs multispectral information beneath cloud cover. Evaluated on Sentinel-2B data, the approach effectively preserves both spatial continuity and spectral consistency of water bodies, significantly outperforming existing techniques across standard image quality metrics as well as hydrology-specific indicators such as water detection indices.

Abstract

Flooding is the most pervasive natural disaster worldwide. Timely and accurate flood inundation mapping are essential for informing disaster risk management. Optical satellite missions provide high-resolution, multispectral observations critical for flood detection and inundation mapping. However, their operational utility is severely constrained by cloud cover during extreme precipitation events. Conventional cloud-removal techniques based on temporal compositing or interpolation often fail to capture inundation dynamics. In this study, we introduce a cloud-removal framework for flood imagery based on Denoising Diffusion Probabilistic Models, leveraging the Masked Diffusion Transformer architecture. The proposed approach exploits self-attention mechanisms to capture wider spatial context and employs masked token modeling to explicitly learn the reconstruction of cloud-obscured regions. Trained on multispectral Sentinel-2B flood scenes with realistic cloud patterns, the model generates cloud-free image realizations that preserve both visual fidelity and hydrological consistency. Reconstruction performance is evaluated using standard image quality metrics alongside flood-specific hydrological measures, demonstrating improved continuity of water bodies and preservation of spectral signatures critical for water detection indices. The results indicate that diffusion-based generative modeling offers a robust and physically consistent alternative for cloud removal in optical flood monitoring, enabling more reliable, continuous observations to support disaster risk management and flood-related decision making.

Problem

• flood inundation mapping
• cloud removal
• optical remote sensing
• disaster risk management
• Sentinel-2

Innovation

• Denoising Diffusion Probabilistic Models
• Masked Diffusion Transformer
• cloud removal
• flood inundation mapping
• multispectral remote sensing