This English-taught Master’s programme trains you to collect, process, analyse and present geoscientific spatial data using remote sensing technologies and contemporary data-processing methods. It combines theoretical models with practical workflows so you can assess geoinformation and communicate results clearly using modern visualisation techniques.
You will build a rigorous foundation in the principles of recording and processing spatial data as captured by remote-sensing instruments, and learn how remotely sensed signals interact with the environment. The curriculum draws on geosciences, computer science, physics, chemistry and biology to quantify environmental processes, and teaches hands-on skills in advanced data methods — notably machine learning and big-data approaches. You will also gain an overview of the range of remote-sensing technologies and how to apply them to specific scientific and applied problems.
A strong emphasis is placed on visualisation and communication: you will use current tools to analyse data, produce forecasts, and prepare outputs tailored to specialists from other disciplines as well as non-specialist decision-makers. The programme aims to deliver an interdisciplinary understanding of geoscientific questions and their presentation for research, policy and applied settings.
The detailed organisation of courses and the full programme structure are provided in the linked PDF. That document is the authoritative source for semester-by-semester module lists, credit allocation (ECTS), examination formats, timetables, and any practical components such as labs, fieldwork or internships.
In general, a Master’s in Remote Sensing, geoInformation, and Visualisation will combine theoretical instruction with hands-on training. Expect core topics and practical labs that develop skills in processing and interpreting earth observation data, building and managing geoinformation systems, and creating effective spatial visualisations. The programme usually culminates in an independent research project or Master’s thesis that integrates methods learned across modules.
Typical key modules and topics you will likely find described in the programme document:
Learning outcomes commonly stated in the programme specification:
Admissions and curriculum details to check in the PDF (concise):
If you’d like, I can extract or rephrase specific module descriptions or learning outcomes from the PDF if you paste its text or upload the file.
All formal academic admission requirements are listed on the programme’s official admission webpage. That page contains the definitive, up-to-date information you must meet to be considered for admission, so please consult it carefully before applying.
International applicants should pay particular attention to sections on eligibility, required application documents, deadlines and language expectations, and should contact the admissions office directly if anything is unclear. Prepare certified copies and translations of documents if needed and allow time for any degree-recognition procedures or visa-related steps.
Winter Semester (International)
Information about the application deadlines can be foundhere.
Graduates will be prepared for technical and analytical roles that require expertise in remote sensing, geoinformation processing, and data visualisation. Typical positions include remote sensing specialist, GIS analyst, geospatial data scientist, and environmental monitoring expert in sectors such as environmental consulting, government agencies, spatial planning authorities, and private companies offering geospatial services.
The programme’s combination of theoretical foundations and practical skills (including machine learning, big data techniques and modern visualisation tools) also makes graduates competitive for research roles at universities and research institutes, jobs with space agencies and satellite data providers, and roles communicating technical results to multidisciplinary teams or non-specialist decision-makers.