Geo-information Processing

What's the Master's Geo-information Science and Earth Observation about?

Based on our ambition of producing actionable geo-information, the department works on the design and development of methods and techniques for processing (acquiring, organizing, analyzing) heterogeneous collections of spatio-temporal data, and in the implementation of open geo-information solutions (models, visualizations and services) that help to understand key societal problems.

As such we operate in the triangle formed between fundamental GIS/RS, domain  applications, and Computer Science & Digital Humanities. For this, we strongly advocate for co-creating knowledge with domain experts to ensure the societal relevance and scientific validity of our work. We link our work to the UT’s Digital Society Institute (DSI).

We are particularly inspired to work on problems arising in societies of the Global South, and as laid out in the UN’s Sustainable Development Goals. Our research specializes around problems that relate to change. For instance, human or goods movements, changes in food and water cycles and implications for impacts on humans and for ecosystems, the spread of infectious diseases, etc.

  • Our Mission

    The Department of Geo-information Processing (GIP) deals with the technological and methodological aspects of geo-information processing and infrastructure to solve complex application problems and to develop technology-oriented concepts for the new geo-information society.

    GIP holds part of the expertise and competence in:

    • Institutional (large volume) geo-information systems (i.e., the design and implementation of large geo-applications with long lifetimes). This part comprises the usual phases of information system design:
      • Requirements analysis;
      • Conceptual design;
      • System development;
      • System implementation;
      • System maintenance.
    • Software engineering for geo-functions entails computer programming methods and techniques in the small, as well as specific, advanced computational techniques of operating on spatial data sets.
    • GDI-operations: Interoperability, OpenGIS, data standards & metadata: The use of geo-spatial data in networked environments provides various challenges, some of which can be addressed by a proper use of available technology. Geodata networks are multi-source, with the source set expected to grow rapidly. This causes problems of data formats, data duplication, redundancy, versioning and data identity
    • GDI-use: Web-based access, dissemination (Interfaces & search mechanism) and usability The Internet, and more particular the world-wide web, is currently the medium to disseminate geo-spatial data and maps, and as such the vehicle of the GDI.
    • Advanced GDI technology - Component GIS & Mobile GIS: This heading deals with the technology (hardware, software) required for putting networked spatial data provision into place. Implementation of
    • GDI - as we currently know it - is one such important issue.
    • Core dataset and multi-scale problems: The type of data that is likely be most transferred over the network will be core or framework data for a particular application. Since the core data will probably be collected at different resolutions, the client will need tools to generalize the data to resolution levels fit for use.
    • Visual representation of geo-spatial data for exploration, analysis and presentation: Maps and graphics are not only used to present known facts about the geodata, but also to explore unknown datasets and extract knowledge from these data.
    • Geo-spatial temporal representations: These days the geoscientist has many data sets available. Many of that data set has a temporal character (think of the satellite imagery collecting every cycle of the satellites).
    • Virtual environments and collaborative visualization: The networked society also allows to communicate and work on a distance. Groups of people can work for instance one the same dataset and discuss solution. Virtual environments play a key role in the collaboration.
    • Visualization of data quality: Today's GIS allow the combination of data sets from sources at different scales in all kind of spatial operations. It is not only important that the result of such analysis is communicated via well designed maps, it is maybe even more important that the user will be able to judge the quality of the result as well.