Big Earth observation analytics to support spatial planning

Suggested by: Martin Sudmanns, Hannah Augustin, Dirk Tiede

Left: Landslide in Bad Hofgastein with large impact on the landscape as seen by an aerial camera. Right: Sen2Cube.at analysis of three years of Sentinel-2 images before and after the event, the RGB composite is based on yearly vegetation layers, where the red colour indicates loss of vegetation after the year 2019 (= landslide extent)

Short description: 

The Sen2Cube.at System is an Earth observation (EO) data cube that allows cloud-based analyses on big EO data (focus on Copernicus Sentinel-2) and producing information that could be of interest for planning purposes and in the contexts of local governments. 

However, using EO data and derived products, particularly for custom, on-demand analysis is challenging and, in the context of local governments, is associated with several hurdles. This master thesis aims to investigate the use of Sen2Cube.at as a cloud-based system for creating custom EO analyses and using them in planning contexts of the local government. The tasks are: 

• Identifying the state-of-the-art and identifying the technical and organizational requirements 
• Developing a workflow for a cloud-based analysis of EO data using the Sen2Cube.at semantic EO data cube using one example (e.g. the landslide in Bad Hofgastein in 2020)  
• Identifying and prototypical development of interfaces into the local government’s workflows by considering their technical requirements previously defined 

The purpose of the master thesis is to create an end-to-end example of such a workflow using a concrete example and detailed documentation in German and English language. Expected is a good technical understanding and an understanding of the requirements and limitations of workflows in local governments. A collaboration with the local government in Salzburg is possible. 

Suggested reading:

Strasser, T., Sudmanns, M., Augustin, H., Van der Meer, L., Herzinger, K., Kerschbaumer, M., ... & Tiede, D. (2022). Identifying soil sealing hotspots on-demand for reporting and decision making in Austria using a Sentinel-2 based semantic EO data cube. In GI_Salzburg 2022. https://sims.sen2cube.at

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807

Start/finish: ASAP

Prerequisites/qualifications: 

• Remote Sensing & GIS 
• Knowledge about spatial planning and local government workflows 
• Interest in the topic   
• German language skills