Training on AI for Disaster Damage Assessment Using Satellite Imagery
Learn AI-powered disaster damage assessment using satellite imagery, machine learning, GIS, and remote sensing for rapid response and recovery planning.
Next intake
20 Jul 2026 · Nakuru
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Intermediate
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
Artificial Intelligence (AI) and satellite imagery are transforming disaster management by enabling rapid, accurate, and scalable damage assessment before, during, and after natural and human-induced disasters. This course equips participants with practical skills in leveraging AI, remote sensing, and geospatial technologies to detect, analyze, and assess disaster impacts on infrastructure, communities, natural resources, and critical services.
Through hands-on exercises, real-world disaster datasets, and practical case studies, participants will learn how to process satellite imagery, apply machine learning and deep learning techniques, automate damage detection, generate impact assessment maps, and support evidence-based disaster response and recovery planning. The course combines AI, GIS, and remote sensing technologies to strengthen disaster risk management and resilience-building initiatives.
Duration
10 Days
Who Should Attend:
- Disaster Management Professionals
- GIS and Remote Sensing Specialists
- Emergency Response Coordinators
- Environmental Officers
- Urban and Regional Planners
- Infrastructure and Utility Managers
- Humanitarian and Development Organizations
- Researchers and Academics
- Government Agencies
- Data Scientists and Geospatial Analysts
What you'll walk away with
By the end of the training, participants will be able to:
- Understand the role of AI and satellite imagery in disaster management.
- Process and analyze satellite imagery for disaster assessment.
- Apply machine learning and deep learning techniques for damage detection.
- Generate disaster impact and damage assessment maps.
- Utilize geospatial analytics to support emergency response and recovery planning.
- Assess the accuracy and reliability of AI-based damage assessment models.
- Develop AI-driven workflows for disaster risk reduction and resilience planning.
What we cover, module by module
Module 1: Introduction to AI, Remote Sensing & Disaster Management
- Fundamentals of AI and machine learning
- Introduction to remote sensing technologies
- Types of disasters and their impacts
- Applications of satellite imagery in disaster management
- AI-driven disaster assessment frameworks
- Case Study: AI-assisted disaster response following a major natural disaster.
- Practical Exercise: Explore disaster-related satellite imagery datasets.
Module 2: Satellite Data Acquisition & Preprocessing
- Sources of satellite imagery
- Optical and radar imagery
- Image correction and enhancement
- Data preparation workflows
- Managing disaster-related geospatial datasets
- Case Study: Preparing satellite imagery for post-disaster analysis.
- Practical Exercise: Preprocess satellite images for damage assessment.
Module 3: GIS for Disaster Risk Assessment
- GIS fundamentals for disaster management
- Spatial data integration
- Hazard and vulnerability mapping
- Exposure analysis
- Geospatial databases
- Case Study: Mapping vulnerable communities using GIS.
- Practical Exercise: Develop a disaster risk assessment map.
Module 4: Machine Learning for Damage Detection
- Supervised and unsupervised learning techniques
- Feature extraction from satellite imagery
- Classification algorithms
- Training and testing datasets
- AI workflows for disaster assessment
- Case Study: Machine learning applications in flood and earthquake damage assessment.
- Practical Exercise: Develop a machine learning model for damage classification.
Module 5: Deep Learning & Automated Damage Assessment
- Deep learning fundamentals
- Convolutional Neural Networks (CNNs)
- Object detection techniques
- Automated infrastructure damage identification
- Large-scale image analysis
- Case Study: Deep learning for building damage assessment after disasters.
- Practical Exercise: Create an automated damage detection workflow.
Module 6: Damage Mapping & Impact Analysis
- Infrastructure damage mapping
- Population and asset impact assessment
- Critical facilities analysis
- Environmental impact mapping
- Reporting disaster impacts
- Case Study: Mapping disaster impacts on transportation and public infrastructure.
- Practical Exercise: Generate disaster damage assessment maps and reports.
Module 7: Change Detection & Rapid Assessment
- Pre- and post-disaster image comparison
- Change detection techniques
- Monitoring recovery efforts
- Time-series analysis
- Rapid assessment methodologies
- Case Study: Post-disaster change detection using satellite imagery.
- Practical Exercise: Conduct change detection analysis for a disaster event.
Module 8: Cloud-Based AI & Geospatial Platforms
- Cloud computing for disaster analysis
- Geospatial AI platforms
- Big data processing
- Collaborative disaster response systems
- Scalable geospatial workflows
- Case Study: Real-time disaster assessment using cloud-based platforms.
- Practical Exercise: Develop a cloud-enabled disaster assessment workflow.
Module 9: Disaster Decision Support & Early Warning Systems
- Decision-support systems
- AI-powered disaster intelligence
- Early warning systems
- Risk communication and visualization
- Disaster preparedness planning
- Case Study: AI-supported emergency management and response planning.
- Practical Exercise: Design a disaster decision-support dashboard.
Module 10: AI-Based Disaster Assessment Project & Action Planning
- Project design and implementation
- Workflow automation
- Performance evaluation
- Stakeholder engagement and reporting
- Future trends in AI for disaster management
- Case Study: Successful implementation of AI-powered disaster assessment systems.
- Practical Exercise: Develop and present an AI for Disaster Damage Assessment Implementation Plan.
Where the change lands
Individual Impact
- Apply AI and geospatial technologies to disaster assessment.
- Improve satellite image interpretation and analytical skills.
- Conduct rapid damage assessments using modern tools and techniques.
- Strengthen expertise in disaster risk management and geospatial intelligence.
Organizational Impact
- Faster and more accurate disaster damage assessments.
- Improved emergency response and recovery planning.
- Enhanced disaster preparedness and risk management capabilities.
- Better allocation of resources through data-driven decision-making.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
- NakuruNext
20 Jul → 31 Jul·In-Person
Book this intake - Kigali
20 Jul → 31 Jul·In-Person
Book this intake - Accra
20 Jul → 31 Jul·In-Person
Book this intake - Kisumu
27 Jul → 07 Aug·In-Person
Book this intake - Johannesburg
27 Jul → 07 Aug·In-Person
Book this intake - Dakar
27 Jul → 07 Aug·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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