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