Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
290226 VU GeoAI and Machine Learning (2025S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 10.02.2025 08:00 bis Mo 24.02.2025 08:00
- Abmeldung bis Mo 31.03.2025 23:59
Details
max. 35 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- N Dienstag 04.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 11.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 18.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 25.03. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 01.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 08.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 29.04. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 06.05. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
- Dienstag 27.05. 10:00 - 12:30 GIS-Labor Geo NIG 1.OG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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 spatial data processing
· Solve practical spatial problems using machine learning and spatial analysis methods
· 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 spatial data processing
· Solve practical spatial problems using machine learning and spatial analysis methods
Mindestanforderungen und Beurteilungsmaßstab
Course grades will be based on exams, class 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 ProgrammingEvaluation measure:
- Timely submission of a project work
- Timely, correct submission of class exercises
- Checks at the end of the units
- Mid-Term Exam: 30 points
- Final Exam: 30 points
- Presentation and project work (given/agreed deadline must be met): 30 points.
- Participation in the discussion phases (constructive, technically correct contributions and engagement in the discussion of the presentations): 10 pointsMore than 50 points (Min 51) are required for a passing grade in the course.
1. 100-90 points
2. 89-80 points
3. 79-70 points
4. 69-51 points
5. 50-0 points
- 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 ProgrammingEvaluation measure:
- Timely submission of a project work
- Timely, correct submission of class exercises
- Checks at the end of the units
- Mid-Term Exam: 30 points
- Final Exam: 30 points
- Presentation and project work (given/agreed deadline must be met): 30 points.
- Participation in the discussion phases (constructive, technically correct contributions and engagement in the discussion of the presentations): 10 pointsMore than 50 points (Min 51) are required for a passing grade in the course.
1. 100-90 points
2. 89-80 points
3. 79-70 points
4. 69-51 points
5. 50-0 points
Prüfungsstoff
* Content of the course units and presentation slides
* A final report (About 10 pages including figures and references) covering the following parts:
- Introduction
- Data
- Methods
- Results and Discussions
- References
* A final report (About 10 pages including figures and references) covering the following parts:
- Introduction
- Data
- Methods
- Results and Discussions
- References
Literatur
**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)
- 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)
Zuordnung im Vorlesungsverzeichnis
(MK2-PI) (MK1-W2-PI)
Letzte Änderung: Di 18.02.2025 14:07
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 spatial AI, including spatial clustering, spatial autoregressive regression, geographically weighted regression and classification methods for spatial 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 spatial data (in vector and raster formats) ready for machine learning workflows with Python programming. We will apply different GeoAI and machine learning algorithms in spatial domain in-practice.
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, Project Presentation and Discussion.