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Teaching with GeoAI

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06-01-2025 08:09 AM
GeriMiller
by Esri Regular Contributor
Esri Regular Contributor
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‎06-01-2025 08:09 AM

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Purpose of Blog

AI has had profound impact on how we do our work. This blog aims at elaborating on how we get started with Teaching with GeoAI in ArcGIS – where do we incorporate in curriculum, what the resources are, as well as possible limitations.  

It is a series of 4 blogs:

How do I get started

  • Educational approaches and objectives vary – please review the resources listed within these blog series to get ideas of what could be applicable to you. For those who favor books, there aren’t too many options on “AI with lessons/hands-on activities” currently. Esri Press is working on “GeoAI – Artificial Intelligence in GIS” book. Consider using pre-trained models available in Living Atlas and tutorials/courses from ArcGIS Tutorials team and Esri Academy – listed in the Resources section.
  • Think about how to prioritize topics or infuse existing content with AI examples – you could be faced with a scenario of “too much content/what to give away” in a course, or better yet, consider how existing topics/concepts could be enhanced/infused with GeoAI approaches.  
  • Think about prerequisites/foundational knowledge - it is very important for students to understand those foundational concepts before using GeoAI tools. This is very much course-objective dependent, but consider prerequisites ahead of time, before modifying any materials.
    • Should students have basic knowledge of GIS/Remote Sensing analytics and technologies (e.g. data visualization, overlay, visual interpretation)?
    • Should students have Programming skills? 
    • Should students have Statistics skills?

Items to keep in mind

  • Use a smaller dataset as proof of concept – works better than complex models. There are several factors contributing to the successful use of pre-trained models. Firstly, the data used is crucial; it is best to use data that is similar in type, resolution, and location to achieve optimal results. Adhering to metadata and guidelines is also important.
  • Be prepared for multiple tries - one pass may not give satisfactory result; it usually takes more than one try. Since one attempt may not suffice, improvements can be made through techniques such as retraining the model, using transfer learning or experimenting with other deep learning models, etc.
  • Ensure students understand limitations of using AI in learning - importance of developing a healthy critical view of data and outputs produced with AI tools. 

Governance, Compliance, Ethics

Check your institutional policies and resources as there is a lot of variation and guidance on using AI, which vary by institution.

  • Check your institutional AI resources (if any exist) – some institutions offer guidance such as the below.
    • AI Guidance, Approved AI Tools, and AI Training
    • AI project checklist to gather information about AI projects
    • AI-related external workshops and webinar links
    • AI Learning Community space to share, learn, and discuss teaching with AI lessons learned, challenges, and solutions.
  • Provide action items and safeguards for students (example items below).
    • Sample Syllabus disclaimers, outlining user responsibility, ethical use, data security and privacy, potential risks, support. Again, those vary institution by institution.
  • IT Risks Assessment Processes (checklists, procedures) – also vary by institution.
    • Department or institution level guidelines.
    • Some institutions employ policies that everyone must undergo, or undergo if certain conditions are met, such as access to institutions systems, logins/authentication, physical hosting of data on institutional IT systems, etc.)

Technology/Hardware

For certain processes/workflows, consider necessary hardware requirements:

  • GPU requirements should be considered
  • If the above is the case, hardware access should be thought of before deploying in class (i.e. would students do work in computer labs, on their own computers, etc.)
  • Additional libraries for performing deep learning in ArcGIS Pro need to be installed before using the tools.

Resources, where to go for help

Feel free to refer to previous blogs in the series:

Key points when thinking about the next steps:

  • Do not be afraid – it is easy to get started, even in introductory courses.
  • The tools are accessible, already within ArcGIS.

Please take a look at the below resources, which could be helpful in the classroom, providing students with an easier learning experience. Feel free to comment on what else could be helpful. 

If you need to speak to a person, please reach out:

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