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ÐÓ°ÉÔ­´´

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GeoAI: Introduction to Deep Learning with Geospatial Data


Transform how you use geospatial data with Machine Learning (ML) & Artificial Intelligence (AI). Most AI courses overlook the complexities of spatial data. This course focuses on practical geospatial workflows you can use in research and industry.

GeoAI mini course
COURSE DETAILS

Dates: August 10–14, 2026
Format: Online (live + guided hands-on)
Time: 11:00 AM – 4:00 PM (ET)

DAILY STRUCTURE

  • 11:00 – 2:00 → Concepts, guided exercises, discussion
  • 2:00 – 4:00 → Independent hands-on (with support)

Follow-up session: August 19 (1-hour Q&A)

WHO SHOULD ATTEND
  • GIS / Remote sensing professionals
  • Environmental professionals
  • Graduate students (Geomatics, Geography, Environmental Science, related disciplines)
  • Researchers & postdocs

Note: Basic understanding of remote sensing is expected

WHAT YOU WILL LEARN
  • How to optimize and tune machine learning and neural network models for different earth observation studies.
  • How to handle spatially auto-correlated data with machine learning models.
  • What are different neural network types and how can you use them with earth observation data.
  • How to use data from foundation models in order to create earth observation products.
COURSE OUTLINE

Day 1: Spatial Autocorrelation & Sampling Design
Day 2: Data Types, Parameter Tuning, & Cross-Validation
Day 3: An Introduction to Neural Networks
Day 4: Gradient Descent Optimization
Day 5: Convolutional Neural Networks & Foundation Models

INSTRUCTOR(S)

This course is delivered by instructors affiliated with ÐÓ°ÉÔ­´´ University, the University of Ottawa, Natural Resources Canada (NRCan), and partner organizations. Instructors bring expertise in GeoAI, remote sensing, and geospatial technologies, combining academic research with hands-on experience in real-world geospatial and environmental applications.

FEES & REGISTRATION

Regular: C$750
Student pricing (open to all institutions): C$250 (limited spots)

(click to the left to register)
Deadline: August 7, 2026
Contact: Ashraf Elshorbagy (ashraf.elshorbagy@carleton.ca)

WHY THIS COURSE?
  • Built specifically for geospatial and environmental workflows
  • Strong balance of theory and hands-on practice
  • Focus on real-world applications, not generic AI
  • Designed for both academia and industry

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