Universität Wien

290703 VU GeoAI and Machine Learning (2026S)

5.00 ECTS (2.00 SWS), SPL 29 - Geographie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The dates are fixed now, but depending on conference participation, emergency meetings, etc., the session on 09.06.2026 may change.

  • Tuesday 10.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 17.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 24.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 14.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 21.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 28.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 05.05. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 09.06. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
  • Tuesday 23.06. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG

Information

Aims, contents and method of the course

Goals:
Geospatial Artificial Intelligence (GeoAI) is an advanced course (graduate-level) in spatial data science covering methods and techniques widely used in Cartography and Geoinformatics. The primary focus of this course is on geospatial data-driven modeling. We will introduce spatially explicit machine learning methods in geospatial AI, including spatial clustering, spatial autoregressive regression, geographically weighted regression and classification methods for geospatial data (e.g., georeferenced social media, GPS trajectories, and remote sensing images) and applications in various domains. We will also introduce spatial data processing methods and tools for getting geospatial data (in vector and raster formats) ready for machine learning workflows with Python programming.

Contents:
In this course different topics will be presented.
1. Introduction to the Artificial Intelligence (AI), GeoAI, and Machine Learning (ML)
2. Different algorithms in ML (Supervised, Unsupervised, Regression, Classification)
3. Geodata preparation for ML
4. Getting to know different types of learning algorithms
5. Introduction to deep learning
6. Explainability in GeoAI

Methods:
Theoretical explanations, Hands-On /Exercises, Group work, Presentation and Discussion.

Assessment and permitted materials

Upon completion of the course, you will be expected to:
· Understand concepts, methods, and techniques in spatial data science
· Be able to construct machine learning workflows on various types of geographic data
· Be familiar with Python programming for geospatial data processing
· Solve practical geospatial problems using machine learning and spatial analysis methods

Minimum requirements and assessment criteria

Course grades will be based on exam, assignments, one final project, and class participation including attendance and group discussion. Assignments will require Python programming based on lecture materials.

Minimum requirement:
- Compulsory attendance - students are allowed two absences
- Presentation of project work is mandatory
- Project work after acquiring competencies from the course
- Basic knowledge of Python Programming

Evaluation measure:
- In class assignment: 10 points (Check exercises at the end of the units)
- Final Exam: 50 points
- Presentation of the project: 30 points.
- Active participation in discussion phases (constructive, technically correct contributions and engagement in the class discussion): 10 points

A positive grade requires an overall score of at least 51%.

More than 50 points are required for a passing grade in the course.
1. 100-88 points
2. 87-76 points
3. 75-64 points
4. 63-51 points
5. 50-0 points

Examination topics

Content of the course units and presentation slides
In class assignments
Presentation of the final project
A final report (About 10 pages including figures and maps):
- Introduction
- Data
- Methods
- Results and Discussions
- References

Reading list

**Books:**
- Song Gao, Yingjie Hu, Wenwen Li. (Eds) (2023) Handbook of Geospatial Artificial Intelligence. CRC Press.
- Stan Openshaw, Christine Openshaw (1997) Artificial Intelligence in Geography. Wiley
**Articles:**
- Song Gao. (2021) Geospatial Artificial Intelligence (GeoAI). Oxford Bibliographies in Geography. 1-16. DOI: 10.1093/OBO/9780199874002-0228
- Krzysztof Janowicz, Song Gao, Grant McKenzie, Yingjie Hu. (2020) GeoAI: Spatially Explicit Artificial Intelligence Techniques for Geographic Knowledge Discovery and Beyond. International Journal of Geographical Information Science. 34(4), 625-636. DOI: 10.1080/13658816.2019.1684500
- Li, W., Arundel, S., Gao, S., Goodchild, M., Hu, Y., Wang, S., & Zipf, A. (2024). GeoAI for Science and the Science of GeoAI. _Journal of Spatial Information Science_, (29), 1-17. - DOI: [10.5311/JOSIS.2024.29.349](http://dx.doi.org/10.5311/JOSIS.2024.29.349)

Association in the course directory

(SDS-IS) (SDS-S) (MK1-W2)

Last modified: Mo 27.07.2026 09:27