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Related Concept Videos

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Related Experiment Video

Updated: Jun 27, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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Published on: April 18, 2025

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

Xin Liang1, Huijiao Qiao1,2, Yanda Chen1

  • 1Taiyuan University of Technology, Taiyuan 030024, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MSS-MambaNet for accurate building extraction from multi-phase disaster imagery. The method enhances semantic segmentation, improving emergency response and post-disaster assessments.

Keywords:
Mambabuilding extractionmulti-phase datamulti-scalenatural disaster

Related Experiment Videos

Last Updated: Jun 27, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Disaster Management

Background:

  • Building extraction from disaster imagery is crucial for emergency response.
  • Multi-phase disaster imagery presents challenges due to cross-phase heterogeneity in building characteristics.
  • Existing methods struggle with stable semantic segmentation under complex damage conditions.

Purpose of the Study:

  • To develop a robust method for building extraction from multi-phase disaster imagery.
  • To address the challenges posed by phase-dependent variations and complex damage conditions.
  • To improve the accuracy and stability of semantic segmentation in disaster scenarios.

Main Methods:

  • Proposed MSS-MambaNet, a novel deep learning architecture for building extraction.
  • Incorporated a multi-scale architecture to enhance perception of diverse building morphologies.
  • Introduced Dual-Domain Cross-Gated Fusion (DDCGF) and Pixel-Aware Dynamic Weighting (PADW) strategies for improved feature discrimination and segmentation consistency.

Main Results:

  • MSS-MambaNet achieved state-of-the-art performance in building extraction from multi-phase disaster imagery.
  • The model obtained an average mIoU of 92.78% and mF1 of 96.25%.
  • Demonstrated effectiveness with only 12.37 million parameters, indicating efficiency.

Conclusions:

  • MSS-MambaNet effectively handles the heterogeneity of multi-phase disaster data.
  • The proposed method provides a stable and efficient solution for building extraction in disaster scenarios.
  • Results highlight the potential for improved emergency response and post-disaster assessment.