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Mapping Historical Landslide Activity Using a Swin Transformer-Based Transfer Learning Approach.

Fei Chen1, Zhihua Liang2, Zhengyuan Cheng2

  • 1School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.

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Summary

This study introduces an advanced model for creating historical landslide inventories, crucial for understanding geological impacts. The developed method efficiently maps landslides over a decade, aiding regional stability analysis.

Keywords:
cross domainhistorical landslidelandslide inventoryswin transformertransfer learning

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Area of Science:

  • Geosciences
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Historical landslide inventories are vital for analyzing landslide patterns and geological impacts.
  • Efficient techniques for historical landslide mapping are underdeveloped, leading to scarce global inventories.
  • Existing landslide detection methods lack efficiency for comprehensive historical mapping.

Purpose of the Study:

  • To develop an advanced landslide detection model for efficient historical landslide mapping.
  • To reconstruct a decadal-scale landslide inventory in the Wenchuan earthquake-affected region.
  • To evaluate the model's performance and the spatiotemporal activity of landslides.

Main Methods:

  • Developed a Swin Transformer architecture integrated with a Pyramid Segmentation Attention mechanism for landslide detection.
  • Employed network fine-tuning for cross-domain adaptation.
  • Applied the model to reconstruct a decadal landslide inventory in the Wenchuan earthquake area.

Main Results:

  • The proposed model demonstrated superior temporal transfer mapping performance compared to state-of-the-art models.
  • The reconstructed historical landslide map exhibited high accuracy and completeness.
  • Landslide activity generally stabilized between 2008 and 2021, influenced by altitude, slope, and aspect.

Conclusions:

  • The developed approach efficiently reconstructs historical landslide inventories, valuable for spatiotemporal analysis.
  • Findings provide crucial insights for regional stability analysis and eco-environment recovery.
  • The study supports informed decision-making in urban planning and infrastructure investments.