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Area of Science:

  • Geosciences
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Landslide mapping is crucial for hazard assessment, but multi-temporal (MT) inventories are scarce.
  • Existing methods often focus on single-event inventories, limiting temporal analysis.
  • Manual landslide mapping is labor-intensive and time-consuming.

Purpose of the Study:

  • To develop an innovative deep learning strategy for generating polygon-based multi-temporal landslide inventories.
  • To adapt an Attention Deep Supervision Multi-Scale U-Net model for landslide detection in new geographical areas.
  • To enable the detection of both rainfall- and earthquake-triggered landslides using a single, adaptable model.

Main Methods:

  • Employed transfer learning with an Attention Deep Supervision Multi-Scale U-Net model for landslide detection.
  • Utilized archived Planet Lab remote sensing images (2009-2021) with 3-5m spatial resolution.
  • Systematically generated multi-temporal landslide inventories and analyzed landslide size distribution.

Main Results:

  • Achieved an average F1 score of 0.8, indicating successful identification of spatiotemporal landslide occurrences.
  • Demonstrated good agreement between predicted co-seismic landslide frequency-area distributions and literature data (power-law exponent difference: 0.04-0.21).
  • Validated the model's effectiveness in mapping landslides across large regions.

Conclusions:

  • The proposed deep learning algorithm effectively generates polygon-based multi-temporal landslide inventories.
  • The method offers flexibility for adapting to new areas and different landslide triggers (rainfall, earthquake).
  • This approach enhances capabilities for large-area landslide hazard assessment and monitoring.