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Related Experiment Video

Updated: Jan 20, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning.

Jhe-Syuan Lai1,2, Fuan Tsai3,4

  • 1Department of Civil Engineering, Feng Chia University, Taichung 40724, Taiwan.

Sensors (Basel, Switzerland)
|August 30, 2019
PubMed
Summary

This study uses machine learning (ML) and satellite data for landslide susceptibility mapping. The developed models achieve high accuracy, exceeding 93% for space-robustness and 75% for time-robustness, proving effective for regional assessments.

Keywords:
GISlandslide susceptibilitymachine learningremote sensingspatial analysis

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Area of Science:

  • Geosciences
  • Remote Sensing
  • Machine Learning

Background:

  • Landslide susceptibility assessments are crucial for disaster risk reduction.
  • Traditional methods often lack the spatial and temporal resolution needed for dynamic event-based analysis.
  • Integrating satellite remote sensing and GIS offers a powerful approach for regional-scale assessments.

Purpose of the Study:

  • To develop a systematic approach using machine learning (ML) for multi-temporal and event-based landslide susceptibility assessments.
  • To evaluate the effectiveness of the Random Forests (RF) algorithm with different sample ratios and cost-sensitive analysis.
  • To assess the spatial and time-robustness of the developed landslide susceptibility models.

Main Methods:

  • Utilized satellite remote sensing images and Geographic Information System (GIS) datasets for spatial analysis.
  • Applied the Random Forests (RF) algorithm, incorporating cost-sensitive analysis for unbalanced sample ratios.
  • Employed space- and time-robustness verification strategies to assess model reliability.
  • Derived 14 GIS-based landslide-related factors for model construction.

Main Results:

  • Achieved high prediction accuracies, with space-robustness verification exceeding 93% and time-robustness verification exceeding 75% in most cases.
  • Demonstrated that multi-temporal models were not significantly affected by variations in sample ratios.
  • Cost-sensitive analysis improved prediction results for unbalanced sample datasets.

Conclusions:

  • The proposed systematic approach integrating ML, satellite remote sensing, and GIS is effective for regional landslide susceptibility assessment.
  • The developed RF models exhibit robust performance in both space and time, providing reliable predictions for future landslide events.
  • The methodology offers a valuable tool for enhancing landslide hazard management and mitigation strategies.