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290169 PS Process Automatisation in Geoinformatics (2024W)
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 02.09.2024 08:00 bis Mo 16.09.2024 12:00
- Anmeldung von Do 19.09.2024 08:00 bis Fr 27.09.2024 12:00
- Abmeldung bis Do 31.10.2024 23:59
Details
max. 35 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 01.10. 08:00 - 10:00 GIS-Labor Geo NIG 1.OG
- Dienstag 08.10. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 15.10. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 05.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 12.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 19.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 26.11. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 03.12. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
- Dienstag 10.12. 08:00 - 10:30 GIS-Labor Geo NIG 1.OG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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
Mindestanforderungen und Beurteilungsmaßstab
The grade is composed of:
• Practical assignments: 45% (15% per assignment)
• 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 unit may be excused.
• Practical assignments: 45% (15% per assignment)
• 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 unit may be excused.
Prüfungsstoff
Provided via e-learning platform as well as documents from the course.
Literatur
- 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
Zuordnung im Vorlesungsverzeichnis
(MK1-W2-PI) (MK2-b-PI)
Letzte Änderung: 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.