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Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.

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Summary

This study introduces an automated method for detecting landslides in mountain cities using remote sensing images. The approach effectively identifies landslide areas by analyzing surface changes and temporal-spatial information, improving disaster preparedness.

Keywords:
Deep Convolution Neural NetworkSpatial Temporal Context Learningchange detectionlandslides detectionremote sensing images

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Area of Science:

  • Geosciences
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Landslides in mountain cities cause significant casualties and economic losses, necessitating precise landslide surveys for disaster management.
  • Traditional landslide detection methods relying solely on spatial information or artificial features often yield poor results due to complex natural environments.
  • Existing approaches struggle to capture the dynamic nature of landslides and their impact on surface cover.

Purpose of the Study:

  • To develop an automated landslides detection approach for mountain cities using pre- and post-event remote sensing images.
  • To leverage surface covering changes and temporal-spatial information for improved landslide identification.
  • To enhance the reliability and completeness of landslide detection in complex terrains.

Main Methods:

  • Utilized Deep Convolutional Neural Network (DCNN) for change detection to identify areas with drastic surface alterations.
  • Employed Spatial Temporal Context Learning (STCL) to pinpoint landslide areas within detected changed regions.
  • Integrated slope degree from Digital Elevation Model (DEM) for result validation and DEM changes for completeness.

Main Results:

  • The automated approach was successfully applied to landslide detection in Shenzhen, Zhouqu County, and Beichuan County.
  • Achieved a commission error for landslide areal extent below 17.6% and a quality percentage above 61.1%.
  • Demonstrated competitive detection percentages for landslide areas, validating the approach's effectiveness.

Conclusions:

  • The proposed approach proves feasible and accurate for detecting landslides in mountain cities.
  • The method effectively utilizes temporal-spatial information and surface change analysis for robust landslide identification.
  • This automated system offers a valuable tool for disaster emergency management in landslide-prone mountainous regions.