290703 SE Emerging Trends in Spatial Data Science (2025W)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 08.09.2025 08:00 to Mo 22.09.2025 08:00
- Registration is open from We 24.09.2025 08:00 to Th 02.10.2025 12:00
- Deregistration possible until Fr 31.10.2025 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 21.10. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 04.11. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 11.11. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 18.11. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 25.11. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 02.12. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 09.12. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 16.12. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 13.01. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 20.01. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
- Tuesday 27.01. 13:00 - 15:00 GIS-Labor Geo NIG 1.OG
Information
Aims, contents and method of the course
The seminar is a scientific discourse about (student-) selected topics approached via individual contributions through presentations/demos, discussions, and a brief written paper or dataset. Topics can vary but are typically at the intersection between geoinformatics and human or physical geography as well as neighboring disciplines such as transportation studies, urban planning, cognitive science, and so on.This year's topic will be about various ethical aspects of GeoAI e.g., bias in data and models.
Assessment and permitted materials
The assessment is done through an oral presentation/demo, participation in discussions, and the creation and demonstration of an open-content dataset. Students work in teams of 2-3 and prepare a proposal presentation (~10-15 min) , final presentation (40min, 10min interactive in-class experiment, 10min discussion), and introduce their dataset (~10-15 min). Students are expected to participate during all presentations, e.g., from other student teams.
Minimum requirements and assessment criteria
For a passing grade the students need to score at least 51% points. The individual grade depends on the quality of the presentation, the written contribution, quality of the demo and data, and the active participation in discussions. Attendance is mandatory, for instance, students cannot miss their own (their team's) presentation.88 – 100 % „Excellent / Sehr gut”
75 < 88 % „Good / Gut”
63 < 75% „Satisfactory / Befriedigend”
51 < 63 % „Sufficient / Ausreichend”
< 51 % „Insufficient / Nicht Ausreichend
75 < 88 % „Good / Gut”
63 < 75% „Satisfactory / Befriedigend”
51 < 63 % „Sufficient / Ausreichend”
< 51 % „Insufficient / Nicht Ausreichend
Examination topics
Oral presentations, written/programmed contributions (e.g., datasets), and active participation in discussions.
Reading list
A literature sign-up sheet will be distributed during the first session. Students typically read 2-4 papers.
Association in the course directory
(MSDS-IS) (MSDS-S) (MK1-W1) (MK1-W2) (MK2) (MK4)
Last modified: We 08.10.2025 09:27