What is it all about?
The Applied Environmental Informatics and Earth Observation program is about describing, understanding and analyzing the complex interactions between humans, nature and technology in an interdisciplinary way. Applied Environmental Informatics acts as a bridge between different disciplines from the fields of geosciences and environmental sciences. During your studies, you will learn methods from applied computer science, measurement methods from the geosciences, geodesy and remote sensing, for example. At the same time, you will learn the basics of environmental sciences.
Important aspects of environmental informatics therefore include, for example
- analytical and statistical evaluation of environmental data and the description of their quality characteristics
- the physical modeling of processes in the natural and built environment
- earth observation technologies (terrestrial, airborne, satellite-based)
- computer science methods and algorithms, including AI methods for the automated analysis of environmental data
- evaluation, discussion and visualization of results, e.g. also with augmented reality systems
- a sense of responsibility in the application and communication of results and decisions
As a basis for this, the degree program has a strong interdisciplinary and transdisciplinary focus and provides a solid, wide-ranging, subject-related education in natural sciences and engineering in the field of environmental sciences and methods of earth observation. Modern data acquisition and evaluation options - including the use of AI methods - for large amounts of data are just as much a part of the portfolio as geostatistical methods, including the spatial relationships of objects in the built and undeveloped environment. This is accompanied by the teaching of interdisciplinary skills, including the responsible and critical assessment of results and their foundations.
A good knowledge of mathematics, physical principles of the environment, interest in computer science and modern methods of data analysis as well as the willingness to work carefully are fundamental for the above-mentioned range of topics.