The fields of earth observation and geo-information science are gradually moving away from the traditional mapping or “inventory” type of science towards the understanding of the processes that shape our environment, predict future effects and provide reliable information to support planning and policy making.
This ten-week course focuses on solving problems within a specific application field. You can choose from a selection of fields and enables you to apply geo-information science and earth observation knowledge in a local (international) context, through Spatio-temporal analysis and the development of models while taking into account socio-environmental drivers.
Applied Remote Sensing for Earth Sciences
THIS TOPIC CONSISTS OF TWO 7 EC COURSES
Spectral Data Processing
This course focuses on remote sensing data processing from multiple missions, with emphasis earth science applications. The backbone is an introduction to scripting: writing own scripts allows to create custom processing solutions, and can also be used to automate the processing of large data sets for earth science applications.
More information can be found in our current Study Guide.
Spectral Geology
This course focuses on the use of spectroscopic methods to obtain geologic information related to, for example, minerals and rocks, mineralised and geothermal systems, soils and other natural materials. It is designed for students with a solid understanding of Earth Sciences who wish to use state-of-the-art spectroscopic methods to analyse the mineral content and texture of samples.
More information can be found in our current Study Guide.
Geoinformatics
THIS TOPIC CONSISTS OF TWO 7 EC COURSES
Acquisition and Exploration of Geospatial Data
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.
In scientific research significant 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 a good overview of acquisition and exploration of geospatial data principles and methods.
More information about the course can be found in our current Study guide.
Scientific Geocomputing
In this course, you learn about developing algorithmic solutions to geospatial problems. Turn-key software systems for geo-information science and Earth observation are functionally powerful but have no instant solution to each geospatial problem. The ability to construct custom solutions is an essential capability 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 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 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 geo-information science and Earth observation like spatial filters, maximum likelihood classification, coordinate transforms and least-squares adjustment. The course’s programming language will be Python.
