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Course: GeoAI for GIS Professionals: From AI Assistants to Spatial Analysis (NC-GIS_GEOAI)
This four-session workshop provides GIS professionals with practical literacy in artificial intelligence applications for spatial analysis and data management. The course takes a broad, yet accessible approach to GeoAI: covering AI assistants for workflow optimization, automated data processing, and traditional machine learning/deep learning applications. Participants will gain hands-on experience with contemporary AI tools while developing critical understanding of ethical considerations and appropriate use cases.
Target Audience
- Current GIS Professionals seeking to integrate AI into their workflows
- GIS Certificate Program alumni and current students
- Spatial data scientists and analysts
- Anyone working with geographic data who wants to understand AI applications
Prerequisites: Intermediate exposure with hands-on application components. Assumes basic GIS knowledge but no programming or AI background required.
Topics Covered
- Defining GeoAI
- Overview of the three GeoAI paradigms:
- AI assistants for GIS workflows
- AI for spacial data processing and automation
- Traditional GeoAI (machine learning/deep learning for spatial analysis)
- Ethical considerations in GeoAI applications
- Bias in training data algorithms
- Privacy concerns with spatial data
- Transparency and explainability
- Responsible AI deployment
- Survey of available AI assistants (ChatGPT, Claude, Gemini, GitHub, Copilot)
- Use cases and limitations
- Overview of AI assistants within the ArcGIS ecosystem and how they support everyday GIS work
- Effective prompt engineering for spatial analysis and GIS-specific tasks
- Using AI assistants for:
- Code generation and debugging (Python, R, ArcPy, Arcade)
- Metadata and documentation creation
- Research synthesis and literature review
- Report writing and interpretation of spatial results
- Strategies for integrating AI assistants into existing GIS workflows
- Critical evaluation of AI-generated outputs and limitations
- Hands-on practice designing prompts for common GIS tasks
- AI Assistants in the Esri Ecosystem
- ArcGIS Online and Map Viewer assistants
- Arcade Assistant
- Item Details Assistant
- ArcGIS Pro Assistants
- Automated geocoding and address parsing
- Data cleaning and standardization with AI
- Natural language processing for spatial text data
- Large language models for qualitative spacial data analysis
- Automated map labeling and annotation
- Integration with existing GIS pipelines (ArcGIS Pro, QGIS, R/Python)
- Quality control and validation workflows
- Hands-on practice: Processing spatial datasets with AI tools
- Introduction to spacial learning concepts
- Common GeoAI applications:
- Image classification and object detection
- Land cover/land use classification
- Predictive spatial modeling
- Change detection
- Overview of deep learning for spatial data
- Available tools and platforms (ArcGIS Pro ML tools, Google Earth Engine, cloud platforms)
- Understanding model outputs and accuracy assessment
- When to use (or not use) machine learning for spatial analysis
- Case studies and practical examples
Learning Outcomes
- Participants will be able to categorize different GenAI (LLMs), GeoAI approaches and articulate ethical considerations for deployment in their organizations.
- Participants will be able to design effective prompts for GIS tasks, use AI assistants to accelerate common workflows, and critically evaluate AI-generated outputs for accuracy and reliability.
- Participants will be able to identify appropriate use cases for AI-assisted data processing nd implement basic automation workflows.
- Participants will be able to identify appropriate applications for spacial machine learning and understand the workflow from data preparation to model deployment.
Teaching Methodology
The course will be taught online through a combination of lectures, demonstrations, and hands-on exercises. Students will work on individual guided projects to apply what they have learned in the course to real-world problems. The course will also include group discussions and peer review to encourage collaboration and critical thinking.
Hardware Requirements
Windows operating system on laptop or desktop required to participate in class. ArcGIS Pro will not work on Apple or Linux operating systems, nor on tablets or mobile devices.
RECOMMENDED HARDWARE
- Windows 10 or 11 (64-bit)
- Intel i5 or AMD equivalent (4+ cores)
- 32 GB RAM (minimum 8 GB)
- 500 GB SSD storage
- Dedicated graphics card (not integrated)