Google Earth Engine for Urban Analysis Training Course
Google Earth Engine (GEE) for Urban Analysis Training Course provides practical, advanced training in geospatial analysis, remote sensing, satellite imagery, and cloud-based geographic information systems for evidence-based urban planning and development.
Skills Covered
Course Overview
Google Earth Engine for Urban Analysis Training Course
Introduction
Google Earth Engine (GEE) for Urban Analysis Training Course provides practical, advanced training in geospatial analysis, remote sensing, satellite imagery, and cloud-based geographic information systems for evidence-based urban planning and development. The course equips participants with the skills to use Google Earth Engine, Landsat, Sentinel-1, Sentinel-2, MODIS, and other Earth observation datasets to analyze urban growth, land-use and land-cover change, vegetation dynamics, surface temperature, built-up areas, environmental conditions, and spatial development patterns. Participants learn to combine geospatial datasets, automate large-scale analysis, develop reproducible workflows, and generate high-quality maps and indicators for urban decision-making.
The course emphasizes practical applications of Google Earth Engine for smart cities, sustainable urban development, climate resilience, infrastructure planning, environmental management, and spatial policy analysis. Through hands-on exercises and global case studies, participants develop competencies in satellite image processing, image classification, change detection, urban heat island analysis, vegetation assessment, urban expansion monitoring, and visualization. The training also demonstrates how Earth observation intelligence can support Public-Private Partnerships (PPP), urban infrastructure investment, climate adaptation, and data-driven development planning.
Course Objectives
By the end of the course, participants will be able to:
- Apply Google Earth Engine for advanced urban geospatial analysis.
- Process and analyze satellite imagery using cloud-based remote sensing workflows.
- Conduct urban land-use and land-cover classification.
- Monitor urban expansion and spatial growth patterns.
- Perform urban heat island and land surface temperature analysis.
- Analyze vegetation, green infrastructure, and environmental change.
- Apply Sentinel-1 and Sentinel-2 data for urban monitoring.
- Develop automated geospatial processing and change-detection workflows.
- Create interactive maps, visualizations, and urban indicators.
- Integrate Earth observation data into smart-city planning.
- Support climate resilience and sustainable urban development analysis.
- Generate evidence for infrastructure planning and Public-Private Partnerships (PPP).
- Interpret geospatial results for strategic urban policy and decision-making.
Organizational Benefits
- Strengthens data-driven urban planning and spatial intelligence.
- Improves monitoring of urban expansion and development patterns.
- Reduces time required for large-scale satellite data processing.
- Enhances climate-risk and environmental assessment capabilities.
- Supports smart-city and sustainable infrastructure initiatives.
- Improves evidence-based infrastructure investment decisions.
- Enables standardized and reproducible geospatial workflows.
- Supports urban development monitoring and compliance.
- Enhances institutional GIS and remote sensing capacity.
- Provides actionable intelligence for Public-Private Partnerships (PPP).
Target Audiences
- Urban planners and city development professionals
- GIS and remote sensing specialists
- Civil engineers and infrastructure professionals
- Environmental and climate-change specialists
- Municipal and government officials
- Real estate and urban development professionals
- Researchers, academics, and development consultants
- Public-Private Partnerships (PPP) and infrastructure professionals
Course Duration: 5 days
Course Modules
Module 1: Google Earth Engine Fundamentals for Urban Analysis
- Google Earth Engine architecture and cloud-based geospatial computing
- Earth Engine Code Editor, JavaScript programming, and data catalogs
- Working with Landsat, Sentinel, MODIS, and global datasets
- Image collections, filtering, compositing, and visualization
- Urban geospatial data preparation and quality control
- Case study: Urban monitoring applications in Singapore
Module 2: Satellite Image Processing for Urban Applications
- Satellite imagery preprocessing and cloud masking
- Sentinel-1, Sentinel-2, and Landsat data processing
- Spectral bands, indices, composites, and image enhancement
- Temporal analysis and seasonal urban monitoring
- Image quality assessment and data validation
- Case study: Satellite-based urban monitoring in London
Module 3: Urban Land-Use and Land-Cover Classification
- Supervised and unsupervised classification techniques
- Training samples and machine-learning classification
- Random Forest classification for urban applications
- Accuracy assessment and classification validation
- Built-up, vegetation, water, and bare-land mapping
- Case study: Land-cover classification in Nairobi
Module 4: Urban Expansion and Change Detection
- Urban growth indicators and spatial expansion analysis
- Multi-temporal satellite image comparison
- Built-up area extraction and change detection
- Urban density and development-pattern analysis
- Mapping informal settlements and peri-urban growth
- Case study: Urban expansion analysis in Lagos
Module 5: Urban Heat Island and Environmental Analysis
- Land Surface Temperature analysis using satellite data
- Urban Heat Island mapping and interpretation
- Relationship between vegetation, buildings, and temperature
- NDVI, NDBI, NDWI, and other urban indices
- Heat-risk visualization for urban planning
- Case study: Urban heat analysis in Dubai
Module 6: Vegetation, Green Infrastructure and Climate Resilience
- Urban vegetation and green-space monitoring
- NDVI-based vegetation health assessment
- Green infrastructure and ecosystem-service analysis
- Flood susceptibility and environmental monitoring
- Climate resilience indicators and spatial assessment
- Case study: Green infrastructure analysis in Melbourne
Module 7: Advanced Urban Analytics and Visualization
- Automated urban analysis workflows and scripting
- Time-series analysis and urban change indicators
- Interactive maps, charts, dashboards, and data visualization
- Exporting imagery, statistics, and analytical outputs
- Integrating Google Earth Engine with GIS workflows
- Case study: Smart-city spatial analytics in Barcelona
Module 8: Applications for Urban Planning and Infrastructure
- Earth observation for infrastructure and development planning
- Spatial intelligence for transport and utility planning
- Supporting sustainable urban development strategies
- Geospatial evidence for investment and Public-Private Partnerships (PPP)
- Developing urban monitoring frameworks and reporting outputs
- Case study: Infrastructure planning and urban resilience in Tokyo
Training Methodology
- Instructor-led presentations and demonstrations
- Hands-on Google Earth Engine practical exercises
- Guided satellite-data processing sessions
- Real-world urban geospatial datasets and assignments
- Interactive discussions and problem-solving activities
- Global case-study analysis and practical interpretation
- Individual and group-based analytical exercises
- Practical project development and presentation
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.