As part of the World Landslide Forum 7 (WLF7), this short course introduces participants to the principles, methods, and tools of Multi-Hazard Risk Assessment (MHRA), with a particular focus on landslide risk within multi-hazard environments and its applications in disaster risk reduction, climate adaptation, and resilience planning. This course is intended for researchers, disaster risk reduction professionals, GIS and remote sensing specialists, engineers, and students interested in understanding and managing risks arising from multiple interacting hazards.

Lead Instructors

Key Topics

1. Foundations of Multi-Hazard Risk Assessment
   • Multi-hazard concepts, terminology, and risk assessment frameworks
   • Hazards, exposure, vulnerability, loss, and damage
   • Impact chains, hazard interactions, and cascading effects
2. Data for Multi-Hazard Assessment
   • Data sources for hazard and risk analysis, including disaster databases and landslide inventories
   • Remote sensing and Earth observation applications
   • Data quality assessment, suitability analysis, and AI-based approaches for multi-hazard assessment
3. Exposure and Vulnerability Assessment
   • Asset classification, building characteristics, and exposure analysis
   • Population datasets and dynamic exposure mapping
   • Physical and indicator-based vulnerability assessment methods
   • Spatial Multi-Criteria Evaluation (SMCE) for risk analysis
4. Risk Assessment Approaches
   • Qualitative and quantitative risk assessment methodologies
   • INFORM Risk framework and other decision-support approaches
   • Project development – making a risk assessment for your own region
5. Practical Tools and Applications
   • Fast Flood and Fast Landslide modeling applications
   • RiskChanges Multi-Hazard Risk Assessment Platform
   • Online geospatial tools for hazard and risk analysis

As part of the 7th World Landslide Forum (WLF7), this workshop provides a comprehensive introduction to the concepts, methodologies, and practical applications of landslide risk assessment. Reliable landslide risk assessment is fundamental for developing effective risk management, mitigation, preparedness, and adaptation strategies, particularly in areas where landslides threaten communities, infrastructure, cultural heritage, and economic activities.

This workshop is intended for researchers, practitioners, civil protection officers, planners, students, engineers, and decision-makers interested in understanding, assessing, and reducing landslide risk.

Lead Instructors

Key Topics

1. Foundations of Landslide Risk Assessment
   • Definitions and concepts of landslide risk & assessment frameworks
   • Hazard, exposure, vulnerability, and consequences
   • Current challenges and uncertainties in risk estimation
2. Data Requirements for Risk Assessment
   • Landslide inventories and event databases
   • Geomorphological and geological information
   • Triggering factors and susceptibility data
   • Population and infrastructure exposure datasets
   • Vulnerability information and historical damage records
3. Risk Assessment Methodologies
   • Geomorphological approaches
   • Statistical & probabilistic approaches
   • Individual and societal risk estimation
4. Risk Communication and Decision Support
   • Landslide risk perception
   • Communication of risk information
   • Supporting land-use planning and policy development
   • Emergency preparedness and civil protection applications
   • Long-term mitigation and adaptation strategies
5. Case Studies and Applications
   • Local and site-specific landslide risk assessments
   • Regional and national-scale assessments
   • Population exposure analysis
   • Applications from Italy and other international contexts

As part of the 7th World Landslide Forum (WLF7), this workshop explores the opportunities, limitations, and future directions of satellite Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) technologies for landslide detection and monitoring. Building on the success of the Sentinel-1 mission and the emergence of next-generation systems such as NISAR, the workshop will examine when radar-based monitoring works effectively, where its limitations lie, and how it can be integrated with optical imagery, LiDAR, and GNSS observations. Through practical examples and expert discussion, participants will gain insights into the evolving role of Earth observation technologies in landslide risk assessment, civil protection, and early warning applications.

Lead Instructor

Key Topics

1. Fundamentals of SAR and InSAR
   • Principles of Synthetic Aperture Radar (SAR)
   • Interferometric SAR (InSAR) techniques
   • Applications in landslide detection and monitoring
   • Factors affecting measurement accuracy
2. Sentinel-1 and a Decade of Systematic Monitoring
   • The impact of the Copernicus Sentinel-1 mission
   • Global monitoring capabilities
   • Advances enabled by open-access radar data
   • Lessons learned from operational applications
3. Understanding the Limitations
   • Vegetation effects and temporal decorrelation
   • Atmospheric disturbances and noise
   • Slope geometry constraints
   • Monitoring fast-moving landslides
   • Examples of successful and unsuccessful applications
4. Comparing Radar Frequency Bands
   • C-band, L-band, and X-band systems
   • Strengths, weaknesses, and complementary capabilities
   • Selecting appropriate systems for different landslide environments
5. Multi-Source Monitoring Approaches
   • Integration with optical remote sensing
   • LiDAR applications for terrain analysis
   • GNSS-based deformation monitoring
6. From Research to Operations
   • Operational landslide monitoring frameworks
   • Civil protection applications and Early warning systems
   • Challenges in implementing large-scale monitoring programs
7. NISAR and Future Perspectives
   • Overview of the NISAR mission
   • Expected advances in landslide monitoring
   • Remaining scientific and operational challenges
   • Future directions for satellite-based hazard assessment

Lead Instructor

Lead Instructors

As part of the World Landslide Forum 7 (WLF7), this short course introduces participants to the principles, methodologies, and practical applications of satellite-based deformation monitoring and artificial intelligence (AI) for detecting, monitoring, and forecasting slow-moving landslides. The workshop focuses on integrating Interferometric Synthetic Aperture Radar (InSAR) observations with machine learning approaches to support landslide hazard assessment, early warning systems, infrastructure management, and risk-informed decision-making.

Slow-moving landslides often remain active for months or years following triggering events such as earthquakes and extreme rainfall, posing long-term threats to communities, transportation networks, critical infrastructure, and economic activities. Recent advances in satellite remote sensing have significantly enhanced our ability to measure ground deformation with millimeter-scale precision over large areas, while developments in AI have created new opportunities to forecast future slope behavior from large deformation datasets.

This course is intended for researchers, geohazard specialists, disaster risk reduction professionals, engineers, GIS and remote sensing practitioners, government agencies, infrastructure managers, and students interested in operational landslide monitoring and forecasting.

Lead Instructor

Key Topics

Fundamentals of Landslide Deformation Monitoring

• Mechanisms and evolution of slow-moving landslides
• Factors controlling slope instability and long-term deformation
• Earthquake- and rainfall-induced slope movements
• Monitoring requirements for hazard assessment and early warning

InSAR for Landslide Detection and Monitoring

• Principles of Interferometric Synthetic Aperture Radar (InSAR)
• Satellite missions and deformation monitoring capabilities
• Mapping active landslides using Sentinel-1 observations
• Generation and interpretation of deformation maps
• Applications in post-earthquake and rainfall-triggered landslide environments
• Limitations, uncertainties, and operational considerations

Deformation Time-Series Analysis

• Extraction and interpretation of displacement time series
• Identification of deformation trends and acceleration phases
• Detection of precursory signals and instability indicators
• Integration of environmental and geospatial datasets

Artificial Intelligence for Landslide Forecasting

• Introduction to machine learning and data-driven forecasting approaches
• Feature engineering and preparation of deformation datasets
• Forecasting future landslide behavior using historical displacement records
• Model validation, performance assessment, and uncertainty analysis
• Operational implementation of forecasting models

Landslide Early Warning and Risk Management Applications

• Integration of monitoring and forecasting systems
• Decision-support frameworks for landslide risk reduction
• Applications for infrastructure management and asset protection
• Emergency response planning and operational early warning systems
• Opportunities and challenges for operational deployment

Practical Exercises and Workflows

• Accessing and visualizing InSAR-derived deformation products
• Identification and mapping of active slow-moving landslides
• Processing displacement time-series datasets
• Preparing datasets for machine learning applications
• Developing and evaluating forecasting workflows
• Reproducible and scalable operational workflows

Learning Outcomes

By the end of the course, participants will:

• Understand the principles and applications of InSAR for landslide monitoring.
• Be able to identify and characterize slow-moving landslides using satellite-derived deformation data.
• Gain practical experience in extracting and interpreting displacement time series.
• Understand the fundamentals of AI-based deformation forecasting.
• Develop and evaluate forecasting workflows using real-world datasets.
• Understand opportunities, limitations, and uncertainties associated with operational forecasting systems.
• Learn how monitoring and forecasting outputs can support landslide early warning systems and risk management.

Lead Instructor