In this module, students learn advanced techniques from big geospatial data management and analysis and are exposed to selected topics in a real-world context on the big geospatial data cluster and beyond. The module introduces examples and the students select one topic and apply this in real world in the seminar running in parallel. Thereby, we bridge the gap between theory and practice and enable students to apply techniques from the field of big geospatial data management in practice. Topics originate from latest research in big geospatial data management as presented on International Conferences such as ICDM, ICDE, and ACM SIGSPATIAL GIS and in journals such as TKDE or GeoInformatica. These topics cover aspects such as data analysis, data distribution, data management, and spatial algorithms.
By completing this module, students will be exposed to state-of-the-art techniques from the quickly evolving field of big geospatial data management thereby deepening their understanding of challenges and solutions in the field of big data and spatial machine learning.
Sessions are anounced via the moodle page.
Content of the Course:
Imporant: All responsibility and liability is with the student group itself
We appreciate your feedback and support. You can drop us a line at any time. If you have interesting examples, you want to share with your fellow students, you can either send it to me via email or create a pull request on GitHub. I would be happy to include your examples, solutions and portations in the lecture.
During the semester, each group works on projects that include fundamentals, as well as state-of-the-art techniques in the field of big geospatial data management. During this course we provide some project ideas, which can be selected. Everyone is welcome to come up with own ideas.
Here is a list of selected projects from previous semesters: