Applied 3D Geological Mapping and Modelling

5 credits

Course, Master's level, 1MP029

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
3 November 2025–18 January 2026
Language of instruction
English
Entry requirements

120 credits of which 90 credits within science/engineering. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Selection

Higher education credits in science and engineering (maximum 240 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 12,083
  • Total tuition fee: SEK 12,083

Read more about fees.

Application deadline
15 April 2025
Application code
UU-18604

Admitted or on the waiting list?

Registration period
20 October 2025–2 November 2025
Information on registration from the department

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
3 November 2025–18 January 2026
Language of instruction
English
Entry requirements

120 credits of which 90 credits within science/engineering. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Admitted or on the waiting list?

Registration period
20 October 2025–2 November 2025
Information on registration from the department

About the course

This course introduces state-of-the-art geological mapping and modelling methods that are currently used by for instance the mining and building industry. These methods include data acquisition with UAVs (Unmanned Aerial Vehicles, e.g. drones), digital outcrop construction, construction of 3D geological maps, and data analysis and modelling. The course will give the opportunity to explore the possibilities of these methods by working on example projects where new data will be collected and combined with existing data. The course mainly uses examples from the mining industry.

No reading list found.

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