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Artificial Intelligence in Remote Sensing for Disaster Management

Edited by Neelam Dahiya, Gurwinder Singh, Sartajvir Singh, and Apoorva Sharma
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394287192  |  Hardcover  |  
366 pages
Price: $225 USD
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One Line Description
Invest in Artificial Intelligence in Remote Sensing for Disaster Management to gain invaluable insights into cutting-edge AI technologies and their transformative role in effectively monitoring and managing natural disasters.

Audience
Professors, undergraduate, graduate, and post-graduate students, PhD researchers and scientists, and industrialists working with integrations for remote sensing

Description
Artificial Intelligence in Remote Sensing for Disaster Management examines the involvement of advanced tools and technologies such as Artificial Intelligence in disaster management with remote sensing. Remote sensing offers cost-effective, quick assessments and responses to natural disasters. In the past few years, many advances have been made in the monitoring and mapping of natural disasters with the integration of AI in remote sensing. This volume focuses on AI-driven observations of various natural disasters including landslides, snow avalanches, flash floods, glacial lake outburst floods, and earthquakes. There is currently a need for sustainable development, near real-time monitoring, forecasting, prediction, and management of natural resources, flash floods, sea-ice melt, cyclones, forestry, and climate changes. This book will provide essential guidance regarding AI-driven algorithms specifically developed for disaster management to meet the requirements of emerging applications.

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Author / Editor Details
Neelam Dahiya, PhD is an assistant professor in the Department of Computer Applications at Chitkara University, Punjab, India. She has authored over ten articles in international journals and filed more than 10 patents with the Indian Patent Office, five of which were granted. She has also reviewed various articles for renowned journals and conferences. Her research interests include remote sensing, digital image processing, deep learning, and hyperspectral imaging.

Gurwinder Singh, PhD is an associate professor at the Institute of Computing at Chandigarh University, India. He has internationally published over 35 articles, conference papers, and book chapters, as well as one patent. He also serves as a member of the International Society for Photogrammetry and Remote Sensing and the Indian Society of Remote Sensing. His research interests include remote sensing, digital image processing, agricultural land use classification, machine learning, and deep learning.

Sartajvir Singh, PhD is a professor and the Associate Director for the University Institute of Engineering at Chandigarh University, Punjab, India. He has filed over 50 patents with the Indian Patent Office, with over half granted. He has authored over 50 articles in international journals and edited various proceedings for conferences and symposia in addition to serving as an editor for several international journals. His research interests include electronics, remote sensing, and digital image processing.

Apoorva Sharma is a digital analyst and assistant professor in the Department of Computer Science and Engineering, Chandigarh University, Punjab, India. She has published three articles in internationally reputed journals and conferences and contributed to innovative wearable and geospatial technologies. Her research interests include remote sensing, digital image processing, agriculture and cryosphere studies, machine learning, and deep learning.

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Table of Contents
Preface
1. Introduction to Natural Hazards, Challenges, and Managing Strategies
Puninder Kaur, Taruna Sharma, Jaswinder Singh and Neelam Dahiya
1.1 Introduction
1.2 Terminology Used
1.2.1 Hazard
1.2.2 Mitigation
1.2.3 Vulnerability
1.2.4 Disaster
1.2.5 Risk
1.3 Classification of Natural Hazards
1.3.1 Biological Natural Hazards
1.3.2 Geological Hazards
1.3.3 Hydrological Hazards
1.3.4 Meteorological Hazards
1.4 Challenges and Risks of Natural Hazards
1.4.1 Loss of Life
1.4.2 Property Damage and Economic Losses
1.4.3 Disruption of Critical Infrastructure
1.4.4 Health Risks and Disease Outbreaks
1.4.5 Environmental Degradation
1.4.6 Social and Economic Disparities
1.4.7 Psychosocial Impacts
1.5 Strategies to Prevent Natural Hazards
1.5.1 Planning and Regulation for Reducing Risk on Land
1.5.1.1 Zoning Regulations
1.5.1.2 Building Codes and Standards
1.5.1.3 Setback Requirements
1.5.1.4 Erosion Control Measures
1.5.1.5 Floodplain Management
1.5.2 Environmental Conservation and Restoration
1.5.2.1 Protecting Natural Ecosystems
1.5.2.2 Restoring Degraded Ecosystems
1.5.2.3 Floodplain Management
1.5.2.4 Coastal Protection
1.5.2.5 Sustainable Land Management
1.5.3 Early Warning Systems and Preparedness
1.5.3.1 Hazard Monitoring and Forecasting
1.5.3.2 Risk Assessment and Planning
1.5.4 Education and Awareness
1.5.4.1 Understanding Hazards and Risks
1.5.4.2 Promoting Risk Reduction Measures
1.5.4.3 School Curriculum Integration
1.5.5 Climate Change Mitigation
1.5.5.1 Reducing Greenhouse Gas Emissions
1.5.5.2 Promoting Renewable Energy
1.5.5.3 Enhancing Energy Efficiency
1.6 Role of Remote Sensing Device to Prevent Natural Disasters
1.6.1 Hazard Detection and Monitoring
1.6.2 Early Warning Systems
1.6.3 Risk Assessment and Vulnerability Mapping
1.6.4 Environmental Monitoring
1.6.5 Mapping and Damage Assessment
1.7 Conclusion
Acknowledgments
References
2. Role of Remote Sensing for Emergency Response and Disaster Rehabilitation
Mochamad Irwan Hariyono and Aptu Andy Kurniawan
2.1 Introduction
2.2 Method
2.3 Disaster Management
2.4 Result and Discussion
2.4.1 Floods
2.4.2 Earthquakes
2.4.3 Drought
2.4.4 Landslides
2.4.5 Land/Forest Fire
2.4.6 Volcanic Eruption
2.5 Conclusion
References
3. Fundamentals of Disaster Management Using Remote Sensing
Garima and Narayan Vyas
3.1 Introduction
3.2 Importance of Remote Sensing in Disaster Management
3.2.1 Role in Emergency Response
3.2.2 Impact on Disaster Rehabilitation
3.2.3 Remote Sensing Taxonomy
3.3 Remote Sensing Applications in Emergency Response
3.3.1 Damage Assessment
3.3.1.1 Techniques and Methods
3.3.1.2 Integration with Other Data Sources
3.3.1.3 Feature Extraction from Pre- and Post-Disaster Imagery
3.4 Acquisition of Disaster Features
3.4.1 Acquisition of Tsunami Features with Remote Sensing
3.4.2 Acquisition of Earthquake Features with Remote Sensing
3.4.3 Acquisition of Wildfire Features with Remote Sensing
Conclusion
References
4. Remote Sensing for Monitoring of Disaster-Prone Region
Navdeep Singh Sodhi and Sofia Singla
4.1 Introduction
4.2 Related Existing Work
4.3 Comparison Table
4.4 Graphical Analysis
4.5 Conclusion and Future Scope
Acknowledgments
References
5. Artificial Intelligence Tools in Disaster Risk Reduction and Emergency Management
Rupinder Singh, Manjinder Singh and Jaswinder Singh
5.1 Introduction
5.1.1 Role of AI Tools and Technologies
5.1.2 Purpose and Objectives of the Research Paper
5.2 AI Tools and Technologies in Disaster Risk Reduction
5.3 Ethical and Social Implications of Using AI Tools in Disaster Management
5.4 Impact and Effectiveness of AI Tools and Technologies
5.5 AI for Dismantling Difficulties in Disaster Management
5.6 Future Directions and Recommendations
5.7 Conclusion
Acknowledgments
Funding
References
6. AI Tools and Technologies in Disaster Risk Reduction and Management
Alisha Sinha and Laxmi Kant Sharma
6.1 Introduction
6.2 AI Tools in Different Phases of Disaster Management
6.2.1 Before Disaster
6.2.2 During Disaster
6.2.3 After Disaster
6.3 Use of Geospatial Technologies and AI in Disaster Management
6.4 Future Challenges and Goals with AI
6.5 Conclusions
Acknowledgment
References
7. AI-Based Landslide Susceptibility Evaluation
Amanpreet Singh and Payal Kaushal
7.1 Introduction
7.2 Principle of Support Vector Machines (SVM)
7.3 Conclusion
Acknowledgments
References
8. Navigating Risk: A Comprehensive Study of Landslide Susceptibility Mapping and Hazard Assessment
Gaurav Kumar Saini and Inderdeep Kaur
8.1 Introduction
8.1.1 Challenges in Factor Selection and Weighting
8.1.2 Combination of Subjective and Objective Approaches
8.2 Factors Responsible for Landslides
8.2.1 External
8.2.2 Internal
8.3 Types of Landslides
8.4 Landslide Detection Techniques
8.5 Landslide Monitoring Techniques
8.6 Use of Machine Learning in Landslide Mapping
8.7 Use of Deep Learning in Landslide Mapping
8.8 Use of Ensemble Techniques
8.9 Limitations of Existing Algorithms
8.10 Dataset Used
8.11 Model Architecture
8.12 Results and Discussion
Acknowledgment
References
9. Application of Geospatial Technology for Disaster Risk Reduction Using Machine Learning Algorithm and OpenStreetMap in Batticaloa District, Eastern
Province, Sri Lanka
Zahir I.L.M., Suthakaran S., Iyoob A.L., Nuskiya M.H.F. and Fowzul Ameer M.L.
9.1 Introduction
9.1.1 Geospatial Technology in DRR
9.1.2 MLAs in DRR
9.1.3 OSM in DRR
9.1.4 Integrated Approach of Geospatial Technology, Machine Learning, and OSM
9.2 Significance of the Study
9.3 Objectives
9.4 Methodology
9.4.1 Study Area
9.4.2 Data Collection
9.4.2.1 MLAs for DRR
9.4.2.2 Integration with OSM
9.5 Results and Discussion
9.6 Conclusion and Recommendations
References
10. Landslide Displacement Forecasting With AI Models
Sangeetha Annam
10.1 Introduction
10.1.1 Technology Classifications for Remote Sensing
10.1.2 Architecture of Risk Management
10.2 Artificial Intelligence-Based Forecasting of Landslide Displacement
10.3 Performance Metrics
10.4 Limitations in Assessing the AI Models for Landslide Displacement Prediction
10.5 Technologies Integrated with AI Models
10.6 Conclusion
References
11. Estimation of Snow Avalanche Hazardous Zones With AI Models
Rajinder Kaur, Sartajvir Singh and Ganesh Kumar Sethi
11.1 Introduction
11.2 Study Site and Data
11.3 Methodology
11.4 Results and Discussion
11.5 Conclusion
References
12. Predicting and Understanding the Snow Avalanche Event
Nitin Arora and Sakshi
12.1 Introduction
12.2 Snow Avalanche
12.2.1 Types of Snow Avalanche
12.2.1.1 Sluff Avalanche
12.2.1.2 Slab Avalanche
12.2.2 Basic Reason Behind Snow Avalanche
12.2.3 Role of Remote Sensing in Snow Avalanche Prediction
12.3 Contributory Factors
12.3.1 Terrain
12.3.2 Precipitation
12.3.2.1 Snow Accumulation
12.3.2.2 Formation of Weak Layers
12.3.2.3 Load and Stress Increases
12.3.2.4 Rain-on-Snow Effect
12.3.3 Wind Temperature
12.3.4 Snowpack Stratigraphy
12.4 Remote Sensing and Avalanche Prediction
12.4.1 Basic Principle Behind Radar-Based Remote Sensing
12.4.2 Need for Remote Sensing
12.5 Methodology
12.5 Conclusion and Future Scope
References
13. A Systematic Review on Challenges and Opportunities in Snow Avalanche Risk Assessment and Analysis
Apoorva Sharma, Bhavneet Kaur and Sartajvir Singh
13.1 Introduction
13.2 Advanced Tools for Snow Avalanche Monitoring System
13.3 Snow Avalanche Risk Assessment and Analysis
13.4 Challenges in Snow Avalanche Risk Assessment and Analysis
13.5 Opportunities in Snow Avalanche Risk Assessment and Analysis
13.6 Summary
References
14. AI-Based Modeling of GLOF Process and Its Impact
Jaswinder Singh, Rajwinder Kaur, Puninder Kaur and Rupinder Singh
14.1 Introduction
14.1.1 The Andes
14.1.2 High Mountain Asia (HMA)
14.1.3 Other Regions
14.2 Artificial Intelligence and GLOF
14.2.1 Modeling the GLOF Process
14.2.2 Impact Assessment
14.2.3 Benefits of Using AI
14.2.4 AI Techniques for the Prediction of GLOF
14.2.4.1 Machine Learning (ML)
14.2.4.2 Deep Learning (DL)
14.2.4.3 Time Series Analysis
14.2.4.4 Integration with Other Techniques
14.3 Machine Learning Techniques for GLOF
14.3.1 Use of Supervised Learning in GLOF
14.3.1.1 Data Preparation
14.3.1.2 Feature Engineering
14.3.1.3 Model Training
14.3.1.4 Prediction
14.3.1.5 Benefits of Using Supervised Learning for GLOF Prediction
14.3.1.6 Various Supervised Algorithms for the GLOF Process
14.3.1.7 Choosing the Right Algorithm
14.3.2 Use of Unsupervised Learning in GLOF
14.3.2.1 Anomaly Detection
14.3.2.2 Feature Discovery
14.3.2.3 Data Preprocessing
14.3.2.4 Unsupervised Learning Algorithms for GLOF Analysis
14.3.2.5 Choosing the Right Algorithm
14.3.2.6 Objective
14.3.2.7 Data Characteristics
14.3.2.8 Benefits of Using Unsupervised Learning for GLOF
14.3.2.9 Challenges and Considerations
14.4 Deep Learning for GLOF Modeling
14.4.1 Convolutional Neural Networks (CNNs)
14.4.2 Recurrent Neural Networks (RNNs)
14.4.3 Combining Different Deep Learning Techniques
14.5 Existing Models for GLOF Modeling: A Comparison
14.5.1 Statistical Models
14.5.2 Machine Learning Models
14.5.3 Deep Learning Models
14.5.4 Comparison
14.5.5 Choosing the Right Model
14.5.6 Additional Considerations
14.6 Future Models for GLOF Modeling
14.6.1 Integration of Diverse Data Sources
14.6.2 Explainable AI (XAI)
14.6.3 Advanced Deep Learning Techniques
14.6.4 Integration with Physical Modeling
14.7 AI Challenges and Limitations
14.8 Insights and Findings from AI-Based Modeling of GLOF Processes
14.9 Evaluation of Methodology Used for AI-Based Modeling of GLOF Processes
14.10 Conclusion
References
15. A Systematic Review of the GLOF Susceptibility Assessment Techniques
Oushnik Banerjee, Anshu Kumari and Apoorva Shamra
15.1 Introduction
15.2 Glacial Lakes in the Western Himalayas
15.2.1 Gangotri Glacier (Supra Glacial Lake)
15.2.2 Samudra Tapu (Pro Glacial Lake)
15.2.3 South Lhonak Lake (Unconnected Glacial-Fed Lake)
15.2.4 Dal Lake (Non-Glacial-Fed)
15.3 Sensitive Glacial Lake in the Western Himalayas
15.3.1 Samudra Tapu Glacier
15.4 GLOF Susceptibility Mapping Techniques
15.4.1 Satellite Imagery Analysis
15.4.2 Semi-Automated GLOF Susceptibility Assessment System
15.4.3 Glacial Lake Mapping
15.5 Stages of Glaciations
15.6 Glacier Retreat
15.7 Causes of Glacial Lake Change
15.8 Depiction and Categorization of Glacial Lakes
15.9 Study of Evaluating Parameters
15.9.1 Sensitivity Evaluation
15.9.2 Calculation of Weights and GLOF Susceptibility Index
15.10 Summary
Acknowledgment
References
16. Challenges of GLOF Estimation and Prediction
Neelam Dahiya, Sartajvir Singh and Puninder Kaur
16.1 Introduction
16.2 Types of GLOF
16.2.1 Glacial Lakes
16.2.2 Moraine-Dammed Lake
16.2.3 Ice-Dammed Lakes
16.3 Reasons for GLOF Occurrence
16.3.1 Glacial Retreat
16.3.2 Geothermal Activity
16.3.3 Avalanches
16.3.4 Earthquakes and Landslides
16.3.5 Human Activities
16.3.6 Glacial Moraine Failure
16.3.7 Glacier Lake Expansion
16.3.8 Glacier Surging and Calving
16.4 Challenges Faced for GLOF Estimation
16.4.1 Early Detection
16.4.2 Infrastructure Damage
16.4.3 Loss of Life
16.4.4 Economic Impact
16.4.5 Environmental Degradation
16.4.6 Climate Changes
16.5 GLOF Solution
16.6 Conclusion
References
17. Real-Time Earthquake Monitoring with Remote Sensing and AI Technology
Koushik Sundar, Narayan Vyas and Neha Bhati
17.1 Introduction
17.2 Basics of AI and Remote Sensing
17.2.1 AI Applications in Earthquake Monitoring
17.2.1.1 Optical Remote Sensing
17.2.1.2 Microwave Remote Sensing
17.2.2 Satellites and Sensors
17.2.3 AI and Remote Sensing for Integration in Monitoring Earthquakes
17.2.4 Challenges and Future Directions
17.3 Advances in Satellite Remote Sensing Techniques for Improved Earthquake Monitoring
17.3.1 Comparative Analysis of Remote Sensing Satellites
17.3.2 Comparison of Optical and Microwave Satellite Imagery
17.3.3 Case Study on Pre- and Post-images of Earthquake in Doti District of Nepal
17.4 How AI Is Currently Being Used in Remote Sensing to Monitor Earthquakes
17.4.1 Automated Image Processing
17.4.2 Seismic Data Augmentation
17.4.3 Risk Assessment and Management
17.4.4 Integrated Monitoring Systems
17.5 Ongoing and Future Practical AI Applications in Remote Sensing
17.5.1 More Sophisticated Prediction Models
17.5.2 Real-Time Data Processing
17.5.3 Damage and Recovery
17.5.4 Public Safety and Community Resilience
17.6 Conclusion
References
18. Enhancing Seismic-Events Identification and Analysis Using Machine Learning Approach
Gurwinder Singh, Harun and Tejinder Pal Singh
18.1 Introduction
18.2 Methodology
18.3 Results and Discussion
18.3.1 ML Models
18.3.2 ARIMA Models
18.3.3 Neural Network Models
18.3.4 Spatial Analysis
18.4 Limitations
18.5 Future Directions
18.6 Conclusion and Future Scope
References
Index

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Table of Contents
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