MSS-MambaNet: A Mamba Framework for Building Extraction from Multi-Phase Disaster Imagery

Building extraction from disaster scenes is critical for emergency response and post-disaster assessment. Unlike conventional static remote sensing imagery, multi-phase disaster imagery contains scenes spanning early, middle, and late disaster stages, where building morphology, class distribution, and boundary characteristics exhibit significant cross-phase heterogeneity. Such phase-dependent variations substantially increase the difficulty of stable semantic segmentation, particularly under complex damage conditions. To address these challenges, we propose MSS-MambaNet for building extraction from multi-phase disaster imagery. A multi-scale architecture is designed to overcome the limitations of single-scale scanning in Mamba, enabling more effective perception of diverse building morphologies. To enhance feature discrimination, a Dual-Domain Cross-Gated Fusion (DDCGF) module is introduced through complementary interactions between spatial and frequency-domain representations. In addition, a Pixel-Aware Dynamic Weighting (PADW) strategy is developed to adaptively emphasize imbalanced foreground pixels and ambiguous boundary regions, thereby improving segmentation consistency under complex disaster conditions. Extensive experiments demonstrate that MSS-MambaNet consistently outperforms state-of-the-art methods, achieving an average mIoU of 92.78% and mF1 of 96.25% with only 12.37 M parameters. These results indicate that the proposed method effectively handles the heterogeneity of multi-phase data, providing a stable and efficient solution for building extraction from multi-phase disaster imagery.