ArticleScientific reports2026
FM-Mamba: end-to-end non-causal Mamba-based network for efficient flood mapping.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate, real-time flood mapping from synthetic aperture radar image is vital for disaster response but is hindered by a persistent trade-off between model accuracy and computational efficiency. This paper introduces FM-Mamba, a novel network that leverages a state space model to break this bottleneck. Its core innovation is an encoder built with non-causal Mamba blocks, which captures essential long-range spatial context with linear complexity, paired with a parameter-efficient decoder designed for precise boundary recovery. Evaluated on the Sen1Floods11 and S1GFloods benchmarks, FM-Mamba achieves leading segmentation accuracy, matching or exceeding state-of-the-art methods in F1-score and IoU. Crucially, it accomplishes this with only 3.93 million parameters and a drastically reduced computational footprint, demonstrating a superior balance of performance and efficiency that is ideal for operational, real-time flood mapping.
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