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September 16, 2025Applied SciencesOpen Access

A Multi-Scale Feature Fusion Dual-Branch Mamba-CNN Network for Landslide Extraction

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Authors

ZYZhiqing YangZHZhang HuaNZNanshan Zheng

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Overview

This method improves landslide detection in remote sensing images, suggesting enhanced recognition of geomorphic features.

Key Points

  • MSCG-Net achieves an IoU score of 78.04% on the Bijie dataset, enhancing landslide identification significantly.
  • By utilizing a dual-branch architecture, MSCG-Net effectively captures spatial features and contextual information.
  • The integration of an adaptive feature enhancement module improves feature representation, supporting better extraction.
  • Comprehensive evaluations indicate that MSCG-Net surpasses existing methods by notable margins in Boundary IoU.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/68d4506b31b076d99fa57992https://doi.org/10.3390/app151810063
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Multi-Scale Feature Fusion Dual-Branch Mamba-CNN Network for Landslide Extraction2025
  2. 2VFM-MoME: A Remote Sensing Landslide Image Segmentation Network Guided by a Visual Foundation Model and a Mixture of Mamba Experts2026
  3. 3MSS-MambaNet: A Mamba Framework for Building Extraction from Multi-Phase Disaster Imagery2026
  4. 4A Deformable Dual-Branch Visual State-Space Network for Landslide Identification with Multi-Scale Recognition and Irregular Boundary Enhancement2026 · 3 citations
  5. 5MSRS-MambaUNet: A multi-source remote sensing model for landslide detection2026