Training on Geospatial AI (GeoAI) Fundamentals: Integrating Machine Learning with GIS Workflows
Learn GeoAI fundamentals by integrating machine learning with GIS workflows for predictive analytics, spatial intelligence, automation, and geospatial decision-
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
Geospatial Artificial Intelligence (GeoAI) is transforming how organizations collect, analyze, and interpret spatial data by combining Geographic Information Systems (GIS), remote sensing, and machine learning technologies. GeoAI enables faster analysis, automated pattern recognition, predictive modeling, and data-driven decision-making across sectors such as urban planning, environmental management, agriculture, infrastructure, public health, and disaster management.
This course equips participants with practical skills in integrating machine learning techniques into GIS workflows to solve complex spatial challenges. Through hands-on exercises, real-world datasets, and practical case studies, participants will learn how to prepare geospatial data, develop machine learning models, automate spatial analysis, generate predictive insights, and build intelligent geospatial solutions that support organizational planning and operations.
Duration
10 Days
Who Should Attend:
- GIS Analysts and Specialists
- Geospatial Data Scientists
- Remote Sensing Professionals
- Urban and Regional Planners
- Environmental Specialists
- Infrastructure and Utility Managers
- Researchers and Academics
- ICT and Data Analytics Professionals
- Government Geospatial Officers
- Development Practitioners
What you'll walk away with
By the end of the training, participants will be able to:
- Understand the fundamentals of GeoAI and its applications.
- Integrate machine learning techniques into GIS workflows.
- Prepare and manage geospatial datasets for AI analysis.
- Apply supervised and unsupervised learning methods to spatial data.
- Develop predictive geospatial models and spatial intelligence solutions.
- Automate geospatial analysis and decision-support processes.
- Implement GeoAI projects to address real-world challenges.
What we cover, module by module
Module 1: Introduction to GeoAI & Intelligent GIS
- Fundamentals of GeoAI
- AI, machine learning, and GIS integration
- Applications of GeoAI across sectors
- GeoAI ecosystem and tools
- Emerging trends in spatial intelligence
- Case Study: GeoAI applications in environmental and urban planning projects.
- Practical Exercise: Explore GeoAI use cases relevant to participants' organizations.
Module 2: Geospatial Data Preparation & Management
- Spatial data types and formats
- Data cleaning and preprocessing
- Feature engineering for geospatial datasets
- Spatial databases and data management
- Data quality assurance
- Case Study: Preparing geospatial data for machine learning projects.
- Practical Exercise: Create and prepare a GIS dataset for analysis.
Module 3: GIS Workflows & Spatial Analytics
- GIS analysis workflows
- Spatial querying and geoprocessing
- Spatial statistics fundamentals
- Data visualization techniques
- Decision-support mapping
- Case Study: GIS-based decision support systems.
- Practical Exercise: Develop a spatial analysis workflow.
Module 4: Machine Learning Fundamentals for GeoAI
- Supervised learning techniques
- Unsupervised learning methods
- Classification and clustering
- Model training and testing
- Performance evaluation metrics
- Case Study: Machine learning applications in spatial analysis.
- Practical Exercise: Build a machine learning model using geospatial data.
Module 5: Predictive Spatial Modeling
- Predictive analytics concepts
- Spatial prediction models
- Risk and suitability analysis
- Spatial forecasting techniques
- Model interpretation
- Case Study: Predicting environmental and infrastructure risks.
- Practical Exercise: Develop a predictive geospatial model.
Module 6: GeoAI for Remote Sensing & Image Analysis
- Remote sensing fundamentals
- Image classification techniques
- Feature extraction from imagery
- AI-powered image analysis
- Land use and land cover applications
- Case Study: Automated satellite image classification using machine learning.
- Practical Exercise: Perform image classification using GeoAI techniques.
Module 7: Automation & Intelligent GIS Workflows
- Workflow automation concepts
- AI-assisted geoprocessing
- Model deployment strategies
- Batch processing and scalability
- Integrating AI into GIS operations
- Case Study: Automating geospatial analysis processes.
- Practical Exercise: Build an automated GIS workflow.
Module 8: GeoAI Applications Across Sectors
- GeoAI in agriculture
- Environmental monitoring
- Urban planning and smart cities
- Disaster risk management
- Public health and infrastructure planning
- Case Study: Cross-sector applications of GeoAI.
- Practical Exercise: Design a GeoAI solution for a sector-specific challenge.
Module 9: Cloud-Based GeoAI & Spatial Intelligence Platforms
- Cloud computing for geospatial analytics
- GeoAI platforms and tools
- Big geospatial data processing
- Collaborative geospatial environments
- Real-time spatial intelligence systems
- Case Study: Enterprise GeoAI implementations.
- Practical Exercise: Develop a cloud-based GeoAI workflow.
Module 10: GeoAI Project Development & Action Planning
- GeoAI project planning and implementation
- Stakeholder engagement
- Performance measurement and evaluation
- Ethical considerations in AI and GIS
- Future trends in GeoAI
- Case Study: Successful GeoAI deployment projects.
- Practical Exercise: Develop and present a GeoAI Implementation Plan integrating machine learning with GIS workflows.
Where the change lands
Individual Impact
- Apply machine learning techniques to geospatial datasets.
- Enhance GIS and spatial analysis capabilities.
- Develop predictive models for location-based decision-making.
- Improve proficiency in modern geospatial technologies.
Organizational Impact
- Enhanced geospatial analytics and operational intelligence.
- Improved planning and decision-making processes.
- Increased efficiency through automated spatial workflows.
- Greater innovation in geospatial data management and analysis.
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.
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