Deep Learning for Remote Sensing to Improve Flood Inundation Mapping

• URL: http://arxiv.org/abs/2606.02310v1
• Date: Mon, 01 Jun 2026 14:28:48 GMT
• Title: Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
• Abstract summary: We introduce a cloud-removal framework for flood imagery based on Denoising Diffusion Probabilistic Models.<n> trained on multispectral Sentinel-2B flood scenes with realistic cloud patterns, the model generates cloud-free image realizations.<n>Results indicate that diffusion-based generative modeling offers a robust and physically consistent alternative for cloud removal in optical flood monitoring.
• Score: 0.09332987715848712
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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