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June 19, 2026SensorsOpen Access

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

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Authors

XLXin LiangHQHuijiao QiaoYCYanda Chen

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Overview

Randomized trial demonstrates effective building extraction in multi-phase disaster imagery, suggesting enhanced emergency response capabilities.

Key Points

  • To develop a framework for accurate building extraction from multi-phase disaster imagery, addressing phase-dependent variations.
  • Proposed MSS-MambaNet architecture featuring multi-scale scanning for diverse building morphologies.
  • Introduced a Dual-Domain Cross-Gated Fusion (DDCGF) module for enhanced feature discrimination.
  • Implemented a Pixel-Aware Dynamic Weighting (PADW) strategy to improve segmentation consistency.
  • MSS-MambaNet achieved an average mIoU of 92.78% and mF1 of 96.25%.
  • Outperformed state-of-the-art methods while maintaining only 12.37 M parameters.

Cite This Study

Liang et al. (2026) studied this question.

synapsesocial.com/papers/6a34df2365a5b0777af2e44ehttps://doi.org/10.3390/s26123868
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