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A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic

Ning Li1, Guangcai Feng1, Yinggang Zhao1

  • 1School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.

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Summary

This study introduces MSFD-Net, a novel deep learning model that fuses Interferometric Synthetic Aperture Radar (InSAR) and topographic data for accurate landslide detection, identifying 254 landslides in China.

Keywords:
InSARlandslide detectionsemantic segmentationtopographic features

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Area of Science:

  • Geosciences
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Landslides pose significant hazards due to human activities and environmental changes.
  • Interferometric Synthetic Aperture Radar (InSAR) is crucial for detecting surface deformation and enabling early landslide warning.
  • Current deep learning models for landslide detection using InSAR data often suffer from limited accuracy due to reliance on single data sources.

Purpose of the Study:

  • To enhance the accuracy of landslide identification by integrating multi-source data.
  • To develop an improved geological hazard detection model for precise landslide mapping.
  • To address the limitations of existing methods in distinguishing landslides from other geological hazards.

Main Methods:

  • Proposed a multi-source data fusion network (MSFD-Net) utilizing a pseudo-Siamese architecture.
  • Extracted texture features from InSAR deformation data and topographic features from elevation data.
  • Fused features at higher levels within the network for comprehensive analysis.

Main Results:

  • MSFD-Net demonstrated superior performance in comparative and ablation experiments.
  • Successfully identified 254 landslides in the Yellow River region using Sentinel-1 SAR data (2018-2020).
  • Detected landslides predominantly occurred at elevations of 2500-3700 m with slopes of 10-30°.

Conclusions:

  • The proposed MSFD-Net effectively improves landslide detection accuracy by fusing InSAR and topographic data.
  • This method offers a promising approach for rapid and accurate wide-area landslide detection.
  • Facilitates timely implementation of preventive and control measures for landslide hazards.