Selected Topics in Big Geospatial Data

Aim of this Lecture

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.

Time Table

First session: Thursday, October 27, 2022 at 10:00 am

The next sessions will be announced as soon as possible.

Information for the Course

Topic: Deadwood reckognition using AI

Context: BB-KI-CHIPS project aims at teaching students to build AI chips.

This semesters aim: Doing a data acquisition and analysis campaign completely. But all steps are already now prepared (we can already start with existing material from the past before looking at our own data)

Content of the Course:

• Two lectures on natural distasters
• Two lectures on computer vision / coding
• The remainder of the semester to
  • learn and code together (rather a hackathon)
  • organize the prerequisites
    • an allowance to take data
    • a flying license at least for a few
    • we already have 13 good drones physically available
    • DJI drone kits (larger ones, two years old)
  • Survey Planning (3D reconstruction)
• At least one test excursion
• At all times you have a flying license, you can get drones

Imporant: All responsibility and liability is with the student group itself

Feedback and Support

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.

Previous Student projects

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:

• Spatial Data Platforms (Twitter)
• Pointcloud Processing Pipeline
• ObservaToriUM (Published on AGIT’2021)
• Forest and vegetation monitoring using Sentinel-2 Imagery in the northern part of Democratic Republic of Congo (Published on AGILE’2021)
• Surface water monitoring of Lake Starnberg (Published on AGIT’2021)
• Analysing the impacts of a choke point in a well-connected road network using big data
• Change detection of air pollution particles from remote sensing data 
• Land surface temperature change detection 
• Synthetic Trajectory Data Generation from Mobility Simulation 
• HPCsim - mobility simulation on raspberry Pi
• Exploring and Visualizing Social Media Text Features from Mental Health Research in Space and Time