Geoinformatics

In this course, you learn about developing algorithmic solutions to geospatial problems. Turnkey software systems for GIScience and Earth Observation are functionally powerful but have no instant solution to each geospatial problem. The ability to construct custom solutions is an essential attribute of the Geoinformatics specialist, who should have competence in addressing geospatial problems by algorithmic solutions. You specifically learn about solution strategies, high-level solution descriptions in pseudo-code, and translations of these into an implementation in some programming language.

We will also discuss the scientific side of programming by an introduction into literate programming, which emphasizes documentation of code and the FAIR principles of scientific data management, which apply to data and code. We will emphasize the role of data in geospatial algorithms, as these are often data-intensive. By reviewing and developing (pseudo-)code, you will increase your understanding of basic concepts in GIScience and Earth Observation. The course’s programming language will be Python, but throughout the Master's, you will learn to implement your algorithms using also other programming and scripting languages.

One driver of today’s information society is geospatial data. Recent years have seen an increase in volume and diversity of geospatial data. In this course, you will use algorithmic thinking and programming skills to find, retrieve, store, and explore various geospatial datasets. A common wisdom of scientific research is that most of time and effort goes into acquiring, understanding, and cleaning the data before the actual analysis begins. Maps and diagrams are not only used to present the final results, but also to verify and explore the data during the whole data processing process phase. After this course, you will have mastered the basics of acquisition and exploration of geospatial data.

This course focuses on using the pre-processed geospatial data for actual extraction and analysis of information. Course topics are methods to find patterns in data (i.e. clustering), to classify data, and to predict unknown values in data, taking uncertainty of measurements and models into account. Basic photogrammetric principles for feature extraction are introduced. To communicate the results you will also learn about geovisualization methods, and more options for storing data effectively and efficiently using specially adopted database structures.

Thanks to the digital, mobile and IT revolutions, massive amounts of data are nowadays collected at unprecedented spatial, temporal, and thematic scales by both physical and human sensors. For most applications, data availability is less of an issue. What remains an issue is how to convert this data into usable and actionable geo-information that supports decision-making at various scales and that can be further processed to generate knowledge. As a consequence, scientific workflows and scientific workflow management systems become more important for knowledge sharing and ensuring reproducibility.

In this course, selected elements of an integrated workflow are introduced. Methods and techniques that can process massive amounts of spatio-temporal data in (quasi-)real time by using cloud computing technologies will be discussed. This course combines and extents knowledge on semantics, linked data, machine learning and distributed databases, and interactive dashboards presented online.

Basic image processing methods such as linear filters, feature based DTM production and conventional hard pixel based classification face limitations making them to be insufficient for reliable geo-information extraction in automatic settings. In this course, you will learn to identify such limitations and will be introduced to more advanced image analysis methods enabling to enrich your geo-information problem solving abilities.