AI-Powered Land Use Classification and Scenario Forecasting Training Course

Introduction

The AI-Powered Land Use Classification and Scenario Forecasting Training Course is a future-forward program designed to equip professionals with cutting-edge tools for analyzing land cover, predicting development trends, and supporting sustainable spatial planning. This training merges artificial intelligence, satellite image processing, and geospatial modeling to generate high-accuracy land use maps and simulate changes over time.

Participants will master the use of machine learning and deep learning models to classify satellite imagery, detect land transformations, and forecast urban sprawl, agricultural expansion, deforestation, and infrastructure development. This course is essential for decision-makers working in land management, environmental policy, and smart city planning.

Target Audience

• Urban Planners and Land Use Managers
• GIS Analysts and Remote Sensing Experts
• Environmental Scientists and Ecologists
• Regional Development Authorities
• Infrastructure and Transport Planners
• Climate Change and Resilience Planners
• Academic Researchers in Geospatial Technologies

Duration

5 Days

Course Objectives

1. Understand land use classification principles and applications
2. Process and classify satellite imagery using AI models
3. Apply supervised and unsupervised classification techniques
4. Forecast future land use scenarios using geospatial models
5. Validate classification results using accuracy metrics
6. Use open-source and cloud-based AI tools for scalability
7. Integrate classification data into spatial planning workflows
8. Develop scenario-based decision support systems

Course Content

Module 1: Fundamentals of Land Use and AI Classification

• Land use vs land cover concepts
• Spatial data sources for land classification
• Overview of AI and deep learning for imagery analysis
• Importance of accurate land use data in planning
• Review of classification workflows

Module 2: Preprocessing and Feature Extraction for Classification

• Radiometric and geometric correction
• Cloud masking and spectral enhancement
• Vegetation and built-up indices (NDVI, NDBI)
• Extracting spatial features and texture
• Data normalization and training sample selection

Module 3: Machine Learning for Land Use Classification

• Random Forest and Support Vector Machines
• Image segmentation and classification trees
• Training, testing, and validation processes
• Accuracy assessment and confusion matrix
• Post-classification processing techniques

Module 4: Deep Learning with Convolutional Neural Networks (CNNs)

• CNN architecture for spatial data
• Using TensorFlow and Keras in land classification
• High-resolution image segmentation
• Object-based image analysis (OBIA)
• Challenges and performance optimization

Module 5: Change Detection and Trend Analysis

• Multi-temporal image comparison
• Change vector analysis and trend mapping
• Detecting urban growth, deforestation, or wetland loss
• Time series analysis of satellite images
• Mapping encroachment and degradation

Module 6: Scenario Modeling and Forecasting Techniques

• Markov Chain and Cellular Automata models
• Integrating socio-economic and spatial drivers
• Simulating multiple land use scenarios
• Assessing environmental and policy impacts
• Visualization of future land use maps

Module 7: Tools and Platforms for AI Land Use Analysis

• Google Earth Engine for classification workflows
• QGIS with Python plugins for modeling
• SNAP and Orfeo Toolbox for preprocessing
• Open-source deep learning libraries
• Cloud computing and geospatial AI scalability

Module 8: Final Application and Project Development

• Select a study area and prepare training data
• Classify current land use using AI
• Forecast land changes for 10+ years
• Create maps, validate outputs, and present scenarios
• Develop a decision-making report for policy use

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank