Intelligent Interpretation for Geological Disasters: From Space-Air-Ground Integration Perspective

Foreword
Preface
Contents
1 Geological Disaster: An Overview
	1.1 Description of Geological Disaster
		1.1.1 Origin of Geological Disaster
		1.1.2 Types of Geological Disaster
	1.2 Risk Assessment and Management
		1.2.1 Disaster Assessment
		1.2.2 Risk Management
	1.3 Research Methods of Geological Disasters
		1.3.1 Research Mode of Ground Equipment-Based
		1.3.2 Research Mode of Remote Sensing-Based
	1.4 Conclusions and Future Directions
		1.4.1 Key Findings and Insights from the Review
		1.4.2 Gaps and Challenges in Current State of Geological Disaster Research and Management
		1.4.3 Future Directions and Opportunities for Advancing Understanding and Addressing Geological Disasters
	References
2 Principles and Methods of Intelligent Interpretation of Geological Disasters
	2.1 Principles of Intelligent Interpretation of Geological Disasters
		2.1.1 Ability of Deep Learning in Feature Extraction of Remote Sensing Images
		2.1.2 Recognizability of Key Features or Patterns of Geological Disasters Based on Deep Learning
		2.1.3 Detectability of Geological Disasters in Historical Image Change Analysis Based on Deep Learning
	2.2 Methods of Intelligent Interpretation of Geological Disasters
		2.2.1 Convolutional Neural Networks
		2.2.2 Deep Generative Models
		2.2.3 Recurrent Neural Networks
		2.2.4 Graph Neural Networks
	References
3 Intelligent Analysis of Multi-source Long-Term Landslide Ground Monitoring Data
	3.1 Introduction
		3.1.1 Background and Significance
		3.1.2 Research Overview
		3.1.3 Research Object and Contents
	3.2 Related Principles and Techniques
		3.2.1 Random Forest
		3.2.2 Long Short-Term Memory Networks
	3.3 Data Acquisition and Model Construction
		3.3.1 Data Acquisition
		3.3.2 Data Pre-processing
		3.3.3 Model Construction
	3.4 Results and Analysis
		3.4.1 Prediction of Trend Landslide Displacements
		3.4.2 Prediction of Periodic Landslide Displacements
		3.4.3 Prediction of Cumulated Landslide Displacements
	3.5 Summary
	References
4 Deep Learning for Long-Term Landslide Change Detection from Optical Remote Sensing Data
	4.1 Introduction
		4.1.1 Background and Significance
		4.1.2 Research Overview
		4.1.3 Research Object and Contents
	4.2 Study Area and Dataset
		4.2.1 Study Area
		4.2.2 Available Data
	4.3 Methodology
		4.3.1 Landslide Recognizing Models
		4.3.2 Data Sampling
		4.3.3 Model Performance Test
		4.3.4 Evaluation Metrics
	4.4 Results
		4.4.1 Data Channel Test
		4.4.2 Temporal Transfer Capability of Models
		4.4.3 Spatio-Temporal Dynamic Detection of Landslides
	4.5 Discussion
	4.6 Summary
	References
5 Deep Learning Based Remote Sensing Monitoring of Landslide
	5.1 Introduction
		5.1.1 Background and Significance
		5.1.2 Research Overview
		5.1.3 Research Object and Contents
	5.2 Related Principles and Techniques
		5.2.1 Faster R-CNN Model
		5.2.2 Graph Convolutional Network
	5.3 Model Construction
		5.3.1 Graph Convolutional Layer
		5.3.2 Feature Pyramid Network
		5.3.3 Faster R-CNN Based on Graph Convolution and Feature Pyramid
	5.4 Experiments
		5.4.1 Experiments Settings
		5.4.2 Dataset Source
		5.4.3 Experiment Content
	5.5 Summary
	References
6 Deep Learning Based Landslide Susceptibility Assessment
	6.1 Introduction
		6.1.1 Background and Significance
		6.1.2 Research Overview
		6.1.3 Research Object and Contents
	6.2 Related Principles and Techniques
		6.2.1 Overview of the Graph
		6.2.2 Graph Convolutional Network
		6.2.3 Convolutional Neural Network
	6.3 Study Site
		6.3.1 Environmental Conditions
		6.3.2 Human Activities
	6.4 Model Construction
		6.4.1 Depthwise Separable Convolution
		6.4.2 Model Structure
		6.4.3 Feature Selection
		6.4.4 Model Training
		6.4.5 Results
	6.5 Summary
	References
7 Deep Learning Based Intelligent Recognition of Ground Fissures
	7.1 Introduction
		7.1.1 Research Background and Significance
		7.1.2 Research Overview
		7.1.3 Research Object and Contents
	7.2 Related Principles and Technologies
		7.2.1 U-Net
		7.2.2 Graph Convolution Network
	7.3 Data Acquisition and Processing
		7.3.1 Data Source
		7.3.2 Data Preprocessing
		7.3.3 Dataset Construction
	7.4 Ground Fissure Segmentation Based on Multiscale Graph Convolution Feature
		7.4.1 Segmentation Framework
		7.4.2 Multiscale Global Reasoning Module
		7.4.3 Graph Reasoning Module
	7.5 Experimental Results and Analysis
		7.5.1 Experimental Environment
		7.5.2 Results and Analysis
	7.6 Summary
	References