Improving GIS-based landslide susceptibility assessments with multi-temporal remote sensing and machine learning

Keyphrases

• Geographic Information System 100%
• Systems-based 100%
• Machine Learning 100%
• Landslide Susceptibility 100%
• Multi-temporal Remote Sensing 100%
• Remote Machine 100%
• Time Robustness 42%
• Robustness Verification 42%
• Sampling Rate 28%
• Landslide 28%
• Spatial Analysis 28%
• Northern Taiwan 14%
• Dataset Analysis 14%
• Event-based 14%
• Decision Boundary 14%
• Machine Learning Techniques 14%
• Landslide Occurrence 14%
• Landslide Inventory 14%
• Model Verification 14%
• Single Event 14%
• Data-centric 14%
• Result Prediction 14%
• Cost Analysis 14%
• Unbalanced Sample 14%
• Multi-temporal Satellite Images 14%
• Non-landslide 14%
• Specific Events 14%
• Random Forest Regression 14%
• Watershed Region 14%
• Random Forest Algorithm 14%
• Satellite Remote Sensing Image 14%
• Multitemporal Model 14%
• Susceptibility Distribution 14%

Earth and Planetary Sciences

• Geographic Information System 100%
• Machine Learning 100%
• Remote Sensing 100%
• Spatial Analysis 66%
• Taiwan 33%
• Satellite Image 33%
• Satellite Remote Sensing 33%