Training on AI & Computer Vision for Urban Growth & Informal Settlement Mapping
Learn AI and computer vision for urban growth monitoring and informal settlement mapping using satellite imagery, GIS, deep learning, and spatial analytics.
Next intake
20 Jul 2026 · Nakuru
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Advanced
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
Rapid urbanization and the expansion of informal settlements present significant challenges for urban planning, infrastructure development, service delivery, and sustainable city management. Advances in Artificial Intelligence (AI), Computer Vision, satellite imagery, and GIS technologies now enable urban planners and geospatial professionals to monitor urban growth, detect land-use changes, and map informal settlements with greater speed, accuracy, and scalability.
This course equips participants with practical skills in applying AI and computer vision techniques to analyze satellite and aerial imagery for urban growth assessment, settlement detection, land-use mapping, and urban development monitoring. Through hands-on exercises, real-world datasets, and practical case studies, participants will learn how to automate feature extraction, classify urban land cover, identify informal settlements, and generate geospatial intelligence products that support evidence-based urban planning and policy development.
Duration
10 Days
Who Should Attend:
- Urban and Regional Planners
- GIS Analysts and Specialists
- Housing and Settlement Officers
- Smart City Professionals
- Infrastructure and Development Planners
- Remote Sensing Professionals
- Government Agencies
- Researchers and Academics
- NGOs and Development Organizations
- Geospatial Data Scientists
What you'll walk away with
By the end of the training, participants will be able to:
- Understand the fundamentals of AI, computer vision, and urban geospatial analytics.
- Process satellite and aerial imagery for urban analysis.
- Apply machine learning and deep learning techniques for urban feature detection.
- Map urban growth patterns and informal settlements using AI.
- Conduct land-use and land-cover classification for urban environments.
- Assess urban expansion trends and development impacts.
- Develop AI-powered urban monitoring and decision-support solutions.
What we cover, module by module
Module 1: Introduction to AI, Computer Vision & Urban Analytics
- Fundamentals of AI and computer vision
- Urban growth and development dynamics
- Geospatial technologies for urban analysis
- Applications of AI in urban planning
- Introduction to urban monitoring frameworks
- Case Study: AI applications in smart city and urban development projects.
- Practical Exercise: Explore urban geospatial datasets and imagery.
Module 2: Satellite & Aerial Imagery for Urban Mapping
- Sources of urban imagery
- Image preprocessing and enhancement
- Spatial resolution and image quality
- Data preparation workflows
- Managing urban geospatial datasets
- Case Study: Preparing imagery for urban growth monitoring.
- Practical Exercise: Process imagery for urban feature analysis.
Module 3: GIS for Urban Growth Analysis
- GIS fundamentals for urban planning
- Urban spatial databases
- Land-use and land-cover mapping
- Spatial analysis techniques
- Urban growth indicators
- Case Study: Monitoring city expansion using GIS technologies.
- Practical Exercise: Develop an urban land-use database.
Module 4: Machine Learning for Urban Classification
- Supervised and unsupervised learning
- Feature extraction techniques
- Classification algorithms
- Model development and validation
- Urban land-cover classification
- Case Study: Machine learning applications in urban land-use mapping.
- Practical Exercise: Build a machine learning model for urban classification.
Module 5: Deep Learning & Computer Vision for Settlement Detection
- Deep learning fundamentals
- Convolutional Neural Networks (CNNs)
- Object detection and image segmentation
- Building footprint extraction
- Automated settlement identification
- Case Study: AI-driven mapping of informal settlements.
- Practical Exercise: Develop a computer vision workflow for settlement detection.
Module 6: Urban Growth Monitoring & Change Detection
- Multi-temporal image analysis
- Urban expansion monitoring
- Change detection techniques
- Growth pattern analysis
- Urban development forecasting
- Case Study: Assessing urban sprawl using AI and satellite imagery.
- Practical Exercise: Perform urban growth change detection analysis.
Module 7: Informal Settlement Mapping & Vulnerability Assessment
- Characteristics of informal settlements
- Settlement classification approaches
- Infrastructure and service accessibility analysis
- Socio-spatial vulnerability assessment
- Risk mapping techniques
- Case Study: Mapping and assessing informal settlements for service planning.
- Practical Exercise: Create an informal settlement vulnerability map.
Module 8: AI-Powered Urban Decision Support Systems
- Urban intelligence platforms
- Geospatial dashboards and visualization
- Smart city analytics
- Infrastructure planning support
- Data-driven urban policy development
- Case Study: AI-supported urban planning and decision-making.
- Practical Exercise: Develop an urban planning dashboard.
Module 9: Cloud-Based Geospatial AI Platforms
- Cloud computing for urban analytics
- AI-enabled geospatial platforms
- Big geospatial data processing
- Automated urban monitoring systems
- Real-time spatial intelligence
- Case Study: City-scale urban monitoring using cloud-based technologies.
- Practical Exercise: Develop a cloud-enabled urban monitoring workflow.
Module 10: Urban Mapping Project & Action Planning
- Urban analytics project planning
- Stakeholder engagement and reporting
- Performance monitoring and evaluation
- Ethical considerations in AI and urban data
- Emerging trends in urban geospatial intelligence
- Case Study: Successful implementation of AI-driven urban monitoring initiatives.
- Practical Exercise: Develop and present an AI & Computer Vision Urban Growth and Informal Settlement Mapping Project.
Where the change lands
Individual Impact
- Apply AI and computer vision techniques to urban mapping projects.
- Improve satellite image interpretation and geospatial analysis skills.
- Automate urban feature extraction and settlement mapping workflows.
- Strengthen expertise in modern urban geospatial technologies.
Organizational Impact
- Improved urban planning and development monitoring.
- Faster identification and mapping of informal settlements.
- Enhanced land-use management and infrastructure planning.
- Better decision-making through AI-driven geospatial insights.
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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We deliver Training on AI & Computer Vision for Urban Growth & Informal Settlement Mapping in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.