This is Mozhdeh Shahbazi 👋 — your friendly, open‑source GeoAI instructor. I am a computer-vision & GeoAI scientist lead at NRCan, and I'm here to help turn spatial data into real insights 🌍 ✨.
The Google Classroom can be accessed via this link
- with Classroom code: ir232wfq
- Please note that the classroom has reached its maximum capacity as of April 2026.
The material related to this Open-Access GeoAI Classroom are all designed for educational use with public access. Please see the License.
This license only applies to all documents produced by the collaborators and contributors of this repository, and it does NOT affect or change, in any way, the original license of any material/code/package/data that is taken/cited from other sources.
While a copy of all material can be found in the classroom environment, they are also accessible directly here:
A quick introduction to python programming
A tutorial to review how performance metrics of a semantic segmentation model are calculated
A tutorial to practice inferring from a deep learning model for image classification
A quick introduction to foundation models
For a more detailed talk on Planaura, please refer to this webinar
Two tutorials on NRCan's Geo Deep Learning (GDL) and Geo-Inference packages with their applications for semantic segmentation
If you are interested in running any of the tutorials included in this repository through an online platform, you can refer to my Lightning.AI studio template. All you need to do is clone this template. The environment is fully set up and ready to run any of the notebooks or the scripts in these tutorials. All tutorials can be run with limited CPU/GPU resources for free if you are eligible for Lightning.AI's free-tier compute credits.
Disclaimer: The reference to Lightning.AI is provided solely as a convenience for users who wish to run these tutorials in a preconfigured environment. This repository and its authors are independent of Lightning.AI. Any mention of or link to Lightning.AI is for informational and educational purposes only and does not imply endorsement, partnership, sponsorship, or affiliation. The authors assume no responsibility or liability for Lightning.AI's services, account eligibility decisions, credit programs, platform behavior, security, availability, privacy practices, or the processing, storage, and handling of user data. Any interaction with Lightning.AI is solely between the user and Lightning.AI. Users are responsible for reviewing Lightning.AI's documentation, privacy policies, and terms of use before using the platform.