The overarching objective of this module is to equip participiants with the ability to handle large geodata and solve common problems efficiently using the open source programming language python. It will teach and apply numerous techniques that will help students to solve complex, yet in modern geodata science common, processing tasks involving a variety of geodata including field measurements, vector data, and satellite data and products. Both offline and cloud-based processing techniques (Google Earth Engine) will be applied, using a wide variety of libraries and tools for manipulating geodata (e.g., OGR, GDAL, geopandas), machine learning (e.g., scikit-learn), image processing (e.g., numpy, scikit-image).
The course is divided into three parts: in part I students will be introduced to the programing language python and will learn to manage large vector and raster datasets efficiently. In part II, students will learn to access Google Earth Engine through the python interface, to acquire knowledge to access the vast amount of data located there and store them locally, as well as to identify processing tasks that can be executed on the server-side before further offline processing. In part III students will over several weeks work independently on larger research problems, and develop a presentation form for their MAP. Nearly all programing tasks/topics will be rooted in the instructors' research domain (Earth Observation, Conservation Biogeography).
Students at all MSc-levels are eligible, but the seminar will be most appropriate for students who have finished most - if not all - of their other course work, as the requested workload is expected to be high: (nearly) weakly homework assignments, midterm exams, and a final exam (Modulabschlussprüfung MAP) will require students to invest substantial amount of time beyond the contact time in the classroom. The seminar explicitly offers the opportunity to develop a MSc-thesis topic that can be conceptualized and already started in part III of the course.
The course is designed for 16 students, and taught in the PC-Lab using departmental infrastructure. However, the use of personal laptops is welcomed as well. Student selection and information about the exercises and exams will be distributed during the first session.