290169 PS Process Automatisation in Geoinformatics (2024W)
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 02.09.2024 08:00 to Mo 16.09.2024 12:00
- Registration is open from Th 19.09.2024 08:00 to Fr 27.09.2024 12:00
- Deregistration possible until Th 31.10.2024 23:59
Details
max. 35 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 01.10. 08:00 - 10:00 GIS-Labor Geo NIG 1.OG
- Tuesday 08.10. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 15.10. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- N Tuesday 05.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 12.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 19.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 26.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 03.12. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Tuesday 10.12. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
This course will be assessed on:
- practical assignments (3 assignments throughout the semester testing your newly acquired skills),
- a group project for which you will need to submit a report and give a presentation, and
- in-class participation
- practical assignments (3 assignments throughout the semester testing your newly acquired skills),
- a group project for which you will need to submit a report and give a presentation, and
- in-class participation
Minimum requirements and assessment criteria
The grade is composed of:
• Practical assignments: 45%
• Final (group) project: 45%
• In-class participation: 10%
A positive completion requires an overall score of 51% and the grades are awarded as per the following scale:
< 50%: Nicht genügend (5)
>50 - 60%: Genügend (4)
>60 - 72,5%: Befriedigend (3)
>72,5 - 85%: Gut (2)
> 85 - 100%: Sehr gut (1)• Presence in classroom is required. A maximum of one units may be excused.
• Practical assignments: 45%
• Final (group) project: 45%
• In-class participation: 10%
A positive completion requires an overall score of 51% and the grades are awarded as per the following scale:
< 50%: Nicht genügend (5)
>50 - 60%: Genügend (4)
>60 - 72,5%: Befriedigend (3)
>72,5 - 85%: Gut (2)
> 85 - 100%: Sehr gut (1)• Presence in classroom is required. A maximum of one units may be excused.
Examination topics
Provided via e-learning platform as well as documents from the course.
Reading list
- John Carucci (2023): Spatial Data for the Enterprise for dummies (free e-book available at: https://www.safe.com/spatial-data-enterprise-for-dummies/).
- FME academy: https://academy.safe.com/
- Andrew Cutts, & Anita Graser. (2018). Learn QGIS : Your Step-by-step Guide to the Fundamental of QGIS 3.4, 4th Edition: Vol. Fourth edition. Packt Publishing. (Chapter 5. Spatial Analysis)
- QGIS Documentation - Chapter 27.5. The model designer
- ArcGIS Pro documentation - ModelBuilder https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/modelbuilder/what-is-modelbuilder-.htm
- FME academy: https://academy.safe.com/
- Andrew Cutts, & Anita Graser. (2018). Learn QGIS : Your Step-by-step Guide to the Fundamental of QGIS 3.4, 4th Edition: Vol. Fourth edition. Packt Publishing. (Chapter 5. Spatial Analysis)
- QGIS Documentation - Chapter 27.5. The model designer
- ArcGIS Pro documentation - ModelBuilder https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/modelbuilder/what-is-modelbuilder-.htm
Association in the course directory
(MK1-W2-PI) (MK2-b-PI)
Last modified: Mo 23.09.2024 13:06
Participants will gain fundamental skills in using FME Desktop, QGIS Model Designer, and ArcGIS Model Builder to automate geospatial tasks. The course will focus on creating and customizing workflows—called workspaces, models, or processes—in these tools to automate data processing tasks efficiently. Central program modules and functions will be introduced, and participants will develop practical know-how through hands-on tasks.
The emphasis is on practical work: students will learn by building and automating processes through a series of exercises and real-world examples. Practical tasks will focus on data transformation, integration, and process automation, demonstrating how to link these tasks into complex workflows across all three platforms.
While classroom sessions focus on practical application, students are responsible for developing the theoretical foundations of each tool with the support of provided materials, such as lecture slides and tutorials.