Multimodal Mamba with multitask learning for building flood damage assessment using synthetic aperture radar remote sensing imagery

Abstract Most post‐disaster damage classifiers perform best when destructive forces leave clear spectral or structural signatures. However, these signatures are often subtle or absent after inundation, where damage may be nonstructural and difficult to detect. Consequently, existing models perform poorly at identifying flood‐related building damage. The model presented in this study, Flood‐DamageSense, addresses this gap as the first deep learning framework purpose‐built for building‐level flood‐damage assessment. The architecture fuses pre‐ and post‐event synthetic aperture radar/interferometric synthetic aperture radar (SAR/InSAR) scenes with very high‐resolution optical basemaps and an inherent flood‐risk layer that encodes long‐term exposure probabilities, guiding the network toward plausibly affected structures even when compositional change is minimal. A multimodal Mamba backbone with a semi‐Siamese encoder and task‐specific decoders jointly predicts (1) graded building‐damage states, (2) floodwater extent, and (3) building footprints. Training and evaluation on Hurricane Harvey (2017) imagery from Harris County, Texas—supported by insurance‐derived property‐damage extents—show a mean F1 improvement of up to 19 percentage points over state‐of‐the‐art baselines, with the largest gains in the frequently misclassified “minor” and “moderate” damage categories. Ablation studies identify the inherent‐risk feature as the single most significant contributor to this performance boost. An end‐to‐end post‐processing pipeline converts pixel‐level outputs to actionable, building‐scale damage maps within minutes of image acquisition. By combining risk‐aware modeling with SAR's all‐weather capability, Flood‐DamageSense delivers faster, finer‐grained, and more reliable flood‐damage intelligence to support post‐disaster decision‐making and resource allocation.