290705 VU Geographic Information Retrieval (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
There is also a class on 22.01.2026.
- Thursday 09.10. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 16.10. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 23.10. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 30.10. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 13.11. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 20.11. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 27.11. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 11.12. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 18.12. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 15.01. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
- Thursday 22.01. 12:30 - 14:30 GIS-Labor Geo NIG 1.OG
Information
Aims, contents and method of the course
The size, modality, and source of information about places and regions across the world are constantly changing. To support downstream tasks like question answering, geographic information retrieval (GIR) provides us with an approach of accessing, searching, indexing, and integrating geographic information. Applications related to GIR include the construction of gazetteers, Web search, and location-based recommendation.The course will cover GIR’s core concepts, techniques and challenges, such as place name ambiguity, georeferencing, indexing and ranking, and natural language processing. Through literature review and Python-based practical exercises, students will learn how to query structured data from geographic databases, how to geoparse and geocode unstructured texts, and how to achieve semantic analysis with retrieved information.Course content from the past year is available at: https://meilinshi.github.io/290061-Geographic-Information-Retrieval/intro.html
Assessment and permitted materials
Participation (10%)
4 quizzes (40%)
1 midterm exam (20%)
1 final exam (30%)
4 quizzes (40%)
1 midterm exam (20%)
1 final exam (30%)
Minimum requirements and assessment criteria
Personal contribution that demonstrates an appropriate understanding of the approaches and methods discussed.100 % - 88 % = 1
87 % - 75 % = 2
74 % - 63 % = 3
62 % - 51 % = 4
Below 51 % = 5
87 % - 75 % = 2
74 % - 63 % = 3
62 % - 51 % = 4
Below 51 % = 5
Examination topics
Core concepts, techniques, challenges, and programming skills in geographic information retrieval
Reading list
1. Purves, R. S., Clough, P., Jones, C. B., Hall, M. H., & Murdock, V. (2018). Geographic information retrieval: Progress and challenges in spatial search of text. Foundations and Trends® in Information Retrieval, 12(2-3), 164-318.
2. Presentation and slides (including references)
2. Presentation and slides (including references)
Association in the course directory
(MSDS-S) (MSDS-IS) (MK1-W1) (MK2)
Last modified: Tu 07.10.2025 10:27