Hands-on tutorials for GeoAI, GIScience, spatial computing, and spatial AI.

This collection curates high-quality, openly available tutorials developed by the community, covering both foundational spatial skills and advanced AI-enabled geospatial workflows.

• Python-based geospatial analysis
• Spatial databases & spatial computing
• GeoAI & spatial machine learning
• Teaching-oriented, reproducible tutorials

To ensure scalability and long-term maintenance, tutorials are organized by topic and level, rather than by author.

tutorials/
├── foundations/ # Core GIS, Python, spatial data fundamentals
├── spatial-computing/ # Spatial SQL, databases, indexing, systems
├── geoai/ # GeoAI, ML/DL, multimodal spatial intelligence
├── visualization/ # Mapping, dashboards, spatial visualization
└── external/ # Curated external tutorials (with attribution)

Each tutorial entry follows a standard metadata format (see template below).

(To be expanded)

An interactive spatial SQL learning environment for hands-on exploration of geospatial databases.

• Repository: https://github.com/rayford295/SpatialQueryLab
• Category: Spatial Computing / Spatial Databases

• Institution: Texas A&M University
• Instructor: Yifan Yang

Core Topics

• Spatial SQL (PostGIS) fundamentals
• Spatial indexing and query optimization
• Query-driven geospatial visualization

Tech Stack

• Supabase (PostGIS)
• Leaflet.js
• HTML, PL/pgSQL

Target Audience

• GIS & geography students
• Spatial database beginners
• Applied spatial analytics learners

Developed for Texas A&M University students, this lab provides an intuitive, browser-based environment for learning spatial database concepts through real-world examples.

An advanced, reading- and lab-driven course introducing GeoAI concepts, methods, and applications at the intersection of geospatial science and artificial intelligence.

• Repository / Course Page: https://github.com/jakobzhao/geog428
• Institution: University of Washington
• Instructor: Dr. Bo Zhao
• Category: GeoAI / Courses / External

Core Topics

• Geospatial machine learning and deep learning
• Large language models (LLMs) for spatial analysis
• Spatial justice, ethics, and algorithmic bias
• Urban, social, and environmental GeoAI applications

Tech Stack

• Python, Jupyter Notebook
• GIS libraries, ML/DL frameworks
• GitHub Pages / GitHub Classroom

Target Audience

• Graduate students and advanced undergraduates
• Researchers interested in GeoAI and spatial intelligence

License

• LGPL-2.1 (repository)

(To be expanded — add external tutorials with attribution and license compliance)

Copy and paste the template below when adding a new tutorial:

#### **Tutorial Name**
One-sentence description of the tutorial.
- **Repository / Link:** URL 
- **Category:** foundations | spatial-computing | geoai | visualization | external 
**Core Topics**
- Topic 1
- Topic 2
**Tech Stack**
- Tools / languages / frameworks
**Target Audience**
- Who this tutorial is for
**License**
- If applicable (e.g., MIT, CC BY 4.0)

• Tutorials may be original, forked, or externally hosted.

• External tutorials must:

  • Retain original licenses
  • Clearly credit the original author(s)
  • Link to the official source

If you would like to contribute, please open an issue or submit a pull request.

This tutorial collection supports the broader AutoGeoAI4Sci initiative by:

• Providing foundational skills needed for GeoAI research
• Bridging spatial computing systems and AI-driven spatial reasoning
• Enabling reproducible, teaching-oriented GeoAI workflows