- Level -Intermediate - Choice - Specialisation
- Understanding how AI is applied in geographical analysis, environmental monitoring, and spatial data processing.
- Prerequisites - Introduction to AI or any foundational AI course
- Assessments -Micro Assessment weekly, Full Assessment last week.
Week 1 - AI in Geographic Information Systems (GIS)
Learning OutcomeUnderstanding how AI enhances GIS-based geographic analysis.
1.1 Basics of GIS and AI Integration.
1.2 AI for Analyzing Satellite and Aerial Imagery.
1.3 AI for Climate and Environmental Data Analysis.
Practical Component
Hands-on analysis of geographic datasets using AI-based GIS tools.
Week 2 - AI for Natural Disaster Prediction and Management
Learning OutcomeExploring how AI helps predict and respond to natural disasters.
2.1 AI in Earthquake and Tsunami Prediction.
2.2 AI for Forest Fire Detection and Management.
2.3 AI in Flood and Cyclone Risk Assessments.
Practical Component
Developing an AI model for natural disaster prediction.
Week 3 - AI in Urban Planning and Smart Cities
Learning OutcomeUsing AI to optimize city planning and infrastructure.
3.1 AI-Based Population and Traffic Analysis.
3.2 Smart City AI Applications in Sustainability.
3.3 AI in Optimizing Energy and Water Usage in Cities.
Practical Component
Using AI tools to simulate smart city planning.
Week 4 - AI in Environmental and Climate Studies
Learning OutcomeUnderstanding how AI contributes to climate and environmental research.
4.1 AI for Air and Water Quality Monitoring
4.2 AI in Biodiversity Conservation and Wildlife Tracking.
4.3 AI in Climate Change Predictions and Mitigation Strategies.
Practical Component
Developing AI-driven environmental monitoring models.
Week 5 - AI in Geopolitical and Economic Geography
Learning OutcomeUnderstanding AIs role in analyzing geopolitical and economic patterns.
5.1 AI in Mapping Global Trade and Supply Chains.
5.2 AI for Predicting Geopolitical Conflicts.
5.3 AI in Studying Migration Patterns and Population Movements.
Practical Component
Using AI to analyze trade and migration trends.
Week 6 - Project and Assessment
Learning OutcomeStudents will develop an AI-driven geography research project.
6.1 Selecting a Geographic Topic and Data Collection.
6.2 Model Development, Self and Peer Assessment.
6.3 Final Presentations and Demonstrations.
Practical Component
Presenting AI-driven geographic analysis findings.