Taylor’s law in satellite time series of vegetation productivity

Background: Taylor’s law is considered a semi-natural-law and originally comes from ecology. It describes how the variance of a quantity grows with its mean, following variance = a · mean^b. It was first found for insect and animal counts, but it also shows up in many other data. The exponent b tells us something about stability. If b is below 2, places with high values fluctuate less, relative to their mean, than places with low values. With Landsat data back to 1985, this can be tested for vegetation at every pixel.

Short description: The thesis tests Taylor’s law with long satellite time series of vegetation, for example from Landsat. For each pixel, a robust yearly vegetation measure is derived, and its mean and variance over the years are calculated. The relationship between mean and variance across all pixels then gives the exponent b. Comparing b between land cover types, regions or elevation zones shows where vegetation is more or less stable. Part of the work is to check how far the results depend on the choice of vegetation index and on the number of available observations.

Taylor’s law from satellite time series (schematic). Each Landsat pixel becomes one point, and the slope b shows how variability scales with productivity.

Suggested reading

Taylor, L. R. (1961). Aggregation, variance and the mean. Nature, 189, 732–735. https://www.nature.com/articles/189732a0

Badgley, G., Field, C. B., & Berry, J. A. (2017). Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances.

Vadrevu, K. (2026): Power law scaling reveals systemic divergence in fire risk across South and Southeast Asian countries. Environmental Research Letters. 10.1088/1748-9326/ae962a

Start: Anytime

Prerequisites/qualification

·        Remote Sensing & GIS

·        Basic programming skills

·        Good basic statistics (regression, power laws)

·        Interest in ecology

Storing Sentinel-2 time series in neural networks: Implicit neural representations

Background: Satellite data are usually stored as a grid with one value per pixel. Implicit neural representations (INRs) instead treat the data as a function of location and time and train a neural network to learn it. You give the network a location and a date, and it returns the values for that place and time. This can save a lot of storage, but the network only stores an approximation. Its quality is usually judged by a single number, the average reconstruction error. What this number hides, which information is lost and where, is largely unknown.

Source: https://gkvoelkl.github.io/python-streamlit-neural-earth-field/en/01-geodaten-als-funktion.html

Short description: The thesis trains an INR on a Sentinel-2 time series of a chosen region and compares the reconstruction with the original data, pixel by pixel and date by date. The aim is to find out what gets lost. Possible candidates are sudden changes (e.g., a meadow mown on a specific date that shows a sharp drop in NDVI), small objects such as single buildings or ponds or extreme values such as the peak of the growing season. The result is a clear picture of which information an INR keeps and which it does not.

Suggested reading

Sitzmann, V., Martel, J., Bergman, A., Lindell, D., & Wetzstein, G. (2020). Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33, 7462–7473.

Madadikhaljan, M., Prexl, J., Wittmann, I., Albrecht, C. M., & Schmitt, M. (2026). Location Is All You Need: Continuous spatiotemporal neural representations of Earth observation data. arXiv:2604.07092.

https://gkvoelkl.github.io/python-streamlit-neural-earth-field/en/01-geodaten-als-funktion.html

Start: Anytime

Prerequisites/qualification: This topic is technically demanding and suits students who enjoy coding and have trained neural networks before.

·        Remote Sensing

·        Very good Python skills and experience with PyTorch

·        Experience training neural networks

Observation density and apparent greening trends in pretrained EO embeddings of alpine ecosystems

Background: Pretrained embeddings such as TESSERA or AlphaEarth Foundations summarise a year of satellite observations in one vector per pixel. Yet the number of clear observations behind each embedding varies between places and years. Bayle et al. (2024) showed that Landsat-based greening trends in the Alps were overestimated because the number of summer observations increased over time. Whether embeddings show the same artefact has not been tested, and in embeddings it could easily go unnoticed.

Short description: The thesis transfers the idea of Bayle et al. to embeddings of alpine vegetation from 2017 onwards. Greening trends are calculated from NDVI and from embeddings for the same pixels, for example along a “greenness axis” in embedding space. A locally runnable model such as TESSERA is then fed with time series that have the same number of observations each year. If trends shrink, part of the greening comes from the data rather than the vegetation. Bare rock serves as a control.

 

Processing chain from satellite remote sensing measuring of Earth's surface reflectance to pixelwise greening trends. What happens if we reconstruct this approach with embeddings? Figure source: Bayle, A. (2026)

Suggested reading:

Bayle, A., Gascoin, S., Berner, L. T., & Choler, P. (2024). Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations. Ecography, 2024(12), e07394. https://doi.org/10.1111/ecog.07394

Bayle, A. (2026): Satellite Remote Sensing of Alpine Vegetation Dynamics: Challenges and Perspectives. Global Change Biology, https://doi.org/10.1111/gcb.70968

Feng, Z., Atzberger, C., Jaffer, S., Knezevic, J., Sormunen, S., Young, R., et al. (2025). TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis. arXiv:2506.20380. https://doi.org/10.48550/arXiv.2506.20380

Start: Anytime

Prerequisites/qualification

• Remote Sensing & GIS
• Good Python skills
• Good basic statistics (e.g., trend analysis)

Measuring ecosystem recovery with spectral indices, fractional cover and semantic categories

Background: After a disturbance such as a landslide, a fire, an avalanche or a wind-throw, satellite data are often used to track how quickly vegetation returns. A common measure is vegetation cover, but it can be described in very different ways. A spectral index such as NDVI gives a unitless value, spectral unmixing gives the share of vegetation in each pixel, and semantic approaches assign a knowledge-based vegetation category. These approaches exist side by side but are rarely compared. Depending on which one is used, the same surface may appear recovered after three years or only after ten.

Schematic figure of ecosystem recovery, here with tree cover as a proxy. How do recovery intervals differ for spectral indices, fractional cover and a semantic approach? Figure source: Mandl et al., 2024

Short description: The thesis tracks recovery on disturbed areas in a region of choice with Sentinel-2 or Landsat time series. Vegetation cover is described in three ways, as NDVI, as vegetation fraction from unmixing, and as the corresponding semantic vegetation category. For each area, recovery is measured as the time until vegetation reaches its pre-disturbance level or a fixed threshold. Very high resolution imagery from different years serves as reference. The result shows how strongly recovery estimates depend on the chosen measure and where the approaches disagree, for example with sparse, dry or patchy vegetation.

Suggested reading

Mandl, L., Viana-Soto, A., Stritih, A., Seidl, R., & Senf, C. (2024). Spectral-unmixing based forest recovery indicators for predicting long-term recovery success. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2024.114194

Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013). Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184–197. https://doi.org/10.1016/j.rse.2013.06.007

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://doi.org/10.3390/rs13234807

Start: Anytime

Prerequisites/qualification: 

·        Remote Sensing & GIS

·        Basic programming

·        Interest in ecosystem dynamics

Three-dimensional surface information in pretrained EO embeddings

Suggested by: Lisa Mandl

Background: Embeddings are derived from images taken from above, but these images contain indirect information about height, such as shadows, texture or radar backscatter from building walls. It is unclear whether embeddings keep this information or mainly encode the land cover type that usually goes with a certain height. AlphaEarth Foundations combines many data sources, including spaceborne lidar, and uses spatial context. TESSERA works pixel by pixel on Sentinel-1 and Sentinel-2 time series.

Short description: The thesis tests how much height information AlphaEarth Foundations and TESSERA embeddings contain, using airborne laser scanning (ALS) data (where available) as reference. Simple models are trained on the embeddings to predict object heights, with classical Sentinel-1 and Sentinel-2 features as a baseline. The key question is whether embeddings capture height differences within a class (e.g., low vs. high buildings) or only between classes. Study area and objects (e.g. buildings, vegetation) are flexible.

Building height map, here for Berlin. How much of height information is stored in geospatial embeddings? Data source: https://zenodo.org/records/4066295#.X4QRNmgzYmI

Suggested reading

Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., et al. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv:2507.22291.

Feng, Z., Atzberger, C., Jaffer, S., Knezevic, J., Sormunen, S., Young, R., et al. (2025). TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis. arXiv:2506.20380. https://doi.org/10.48550/arXiv.2507.22291

Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. https://doi.org/10.1016/j.rse.2020.112128

Start: As soon as possible

Prerequisites/qualification

• Remote Sensing
• Good Python skills + basic machine learning
• Interest in the topic 

Next-Generation EO Archive Storage: Evaluating Zarr and Icechunk for Temporal Access Efficiency and Data Maintainability in Sen2Cube.at

 Suggested by: Luke McQuade, Martin Sudmanns

Short description

Assess Zarr + Icechunk as a future storage option for Sen2Cube

Cloud-optimized GeoTiff (COG) is now an established format for archived EO data, offering reduced data transfer costs when afforded by the user requirements, e.g., area of interest (spatial subsetting) and spatial resolution (overviews/pyramids). However, there are downsides to this format. Typically, COGs are stored as a collection of files - one for each acquisition - and for time series analyses, this means having to open several files, and make several network requests, for even tiny AoIs.

Zarr is a recent alternative, offering chunking across the time dimension as well (and others). Could this be an improvement over COG for Sen2Cube?

Even though data are treated as historical as soon as they enter an archive, occasionally defects are encountered and have to be rectified, or enhancements (to metadata especially) must be made. Icechunk offers a solution for making such changes without having to reprocess lots of data. Could this be used, in conjunction with Zarr, to improve the updateability of Sen2Cube?

Start

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Programming (e.g., Python)
• Interest in the topic 

Semantic vs. Index-Based Bare Soil Mapping from Sentinel-2 Time Series: A Comparative Analysis Using Sen2Cube.at

 Suggested by: Dirk Tiede, Martin Sudmanns

Bare soil mosaic calculation based on semantic categorie in a semantic EO data cube

Short description

Mapping of bare soil exposure across agricultural landscapes is essential for monitoring soil erosion risk, estimating carbon stocks, and evaluating the effectiveness of soil conservation practices such as cover cropping. Reliable and temporally dense bare soil information furthermore provides the foundation for downstream tasks like soil organic carbon modelling and cropland management assessment at regional to national scales.

This thesis topic proposes to investigate whether semantic, knowledge-driven classification within the Sen2Cube.at Earth observation data cube framework can produce comparable bare soil composites from Sentinel-2 time series in contrast to conventional index- and threshold-based approaches. Rather than relying on normalised spectral indices such as NDVI or NBR2 - which lose absolute reflectance intensity, are influenced by clouds and may misclassify sparsely vegetated or mixed pixels as bare soil - the semantic approach encodes expert knowledge about surface conditions directly into the classification logic, potentially leveraging the full spectral profile of each observation. The resulting bare soil composites will be compared against established index-threshold products, such e.g. as the soil spectral suite developed at DLR, using both quantitative accuracy assessment against reference data and a qualitative analysis of how each method handles spectrally ambiguous surfaces. The comparison will specifically examine whether it produces more temporally consistent composites under varying conditions and different time periods.

Suggested reading

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

Heiden, U., d’Angelo, P., Schwind, P., Karlshöfer, P., Müller, R., Zepp, S., Wiesmeier, M., & Reinartz, P. (2022). Soil Reflectance Composites—Improved Thresholding and Performance Evaluation. Remote Sensing, 14(18), 4526. DOI: 10.1016/j.rse.2017.11.004

Start

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Basic Scripting/Programming (e.g., Python)
• Interest in the topic 

Semantic analysis of gradual tree vitality loss using Sen2Cube.at

 Suggested by: Martin Sudmanns, Dirk Tiede

Abrupt changes in vegetation are easy to spot, gradual changes; however, such as those resulting from disease are difficult to identify.

Short description

Understanding the gradual loss of tree vitality is essential for sustainable forest management, particularly in the face of climate change and other environmental stressors. Long-term trends in forest health can be analysed using Earth observation data. Sen2Cube.at is a semantic EO data cube, that enables the analysis of large volumes of Sentinel-2 data semantically enriched over extended periods to monitor changes in forest vitality.

The goal of this master thesis is to develop a workflow for detecting and analysing temporal changes in forest vitality using the Sen2Cube.at . This study, to be conducted in collaboration with the “Bundesforschungszentrum für Wald” will focus on making use of spectral indices and semantic layers to assess vitality loss in forests, with a specific focus on gradual changes.

A specific forest area will be selected as a case study, with results validated using in-situ measurement. The outcomes of this thesis are expected to enhance the understanding of long-term forest health trends and support evidence-based forest management strategies.

Suggested reading

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

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Basic Scripting/Programming (e.g., Python)
• Interest in the topic 

Multiscale analysis of thermal data for urban sustainability

 Suggested by:  Dirk Tiede, Martin Sudmanns

Example data for a thermal acquisition during a hot summer day in a city

Short description

Understanding urban heat dynamics is critical for addressing challenges related to climate change, urban planning, and sustainability. Thermal data from various sources, including satellite imagery and aerial photographs, provide valuable insights into phenomena such as heat islands and green space changes. By analysing these data at multiple scales and times, it is possible to assess and monitor urban thermal behaviour more effectively.

The goal of this master thesis is to perform a multiscale analysis of thermal data as part of the funded project “Prometheus” – Progressive Methods of Thermal High-Resolution Earth Surveillance for Urban Sustainability. The study may focus on one or many of the following topics (tbd):

• Comparing day and night thermal data to analyze urban heat distribution patterns.
• Delineating urban heat islands based on thermal data pattern (spatial/temporal), urban structure and additional data integration
• Investigating the relationship between heat islands and changes in urban green spaces over time.
• Combining thermal data from different scales, such as high-resolution aerial imagery and satellite data, to enhance spatial and temporal understanding.

The outcomes are expected to contribute to sustainable urban planning strategies by providing actionable insights into heat management and green infrastructure optimization. The results will be documented with clear methodologies to support further applications in urban sustainability projects.

 Start

As soon as possible

Prerequisites/qualification

++ interest in the topic
+ programming skills
+ Earth observation

Change analyses: Soil sealing

 Suggested by: Martin Sudmanns, Dirk Tiede

Example of an analysis result that indicates vegetation loss or soil sealing.

Short description

The process of covering natural soil with impervious materials such as concrete or asphalt, in other words soil sealing, is a critical issue in urban planning and environmental management. Monitoring and analysing changes related to it over time is essential for sustainable land-use planning and for supporting authorities in decision-making processes.

This master thesis aims on analysing changes in soil sealing using Earth observation data and geospatial techniques. By employing advanced (geo)visualization methods, the study has the objective to provide intuitive and actionable insights into temporal patterns of soil sealing. The work will explore how these analyses can be effectively integrated into planning workflows and applied to support authorities in managing land-use changes. The results are expected to enhance urban planning strategies by offering tools for better understanding and mitigating the impacts of soil sealing on ecosystems and urban environments.

Suggested reading

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

As soon as possible

Prerequisites/qualification

++ interest in the topic
++ programming skills
+ Earth observation

Time Series Analysis for Natural Hazard Monitoring

 Suggested by: Karima Hadj-Rabah

These images are displacement mean velocity maps of “Mitidja” Bassin (Algeria), using two different time series analysis techniques Permanent Scatterers (PS) on the left and Small Baseline Subset (SBAS) on the right. (c) LTIR/USTHB

Short description

Understanding long-term ground displacement is essential for assessing the impacts of environmental changes and human activities on land stability. Differential Interferometric Synthetic Aperture Radar (DInSAR) time series analysis provides a powerful method for detecting and quantifying such displacements over extended periods. This approach is particularly relevant for studying phenomena like subsidence caused by groundwater extraction and reduced precipitation, as seen in regions affected by similar dynamics to the Mitidja Basin in Algeria.

This master thesis focuses on using DInSAR time series analysis to monitor long-term displacement associated with natural hazards and environmental changes. By leveraging Sentinel-1 SAR data and open-source tools such as GMTSAR and LicSAR, the research aims to develop a workflow for detecting and analyzing ground displacement trends. The outcomes will provide valuable insights into the spatial and temporal dynamics of land deformation, contributing to sustainable land management and hazard mitigation strategies.

Start

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Interest in the topic
• Basic Scripting/Programming (e.g., Python) 

Generating displacement maps for mapping geohazards using Sentinel-1 images

Suggested by: Karima Hadj-Rabah

The two images are displacement maps of “Thessaly” earthquake (Mar.2021), the left one is according to the radar line of sight while the right one is according to the radar platform movement direction. (c) LTIR/USTHB

Short description

Ground displacement caused by geohazards such as earthquakes, volcanic eruptions, and landslides is very challenging to monitor using traditional methods (GNSS, geotechnical instruments,...). Satellite radar images, such as those provided by Sentinel-1, offer a valuable way to detect and measure ground movements over large areas. Two main techniques can be used for that purpose: Differential Interferometric Synthetic Aperture Radar (DInSAR) and Multiple Aperture Interferometry (MAI) to map displacement with high accuracy.

 This master thesis objective is to create a workflow for generating ground displacement maps using Sentinel-1 images in order to support geohazard monitoring and management. The tasks to be accomplished are:

• Reviewing the state-of-the-art in geohazard displacement mapping using SAR data, with a focus on DInSAR and MAI techniques.
• Developing a workflow for processing Sentinel-1 images to generate displacement maps, using free tools such as ESA SNAP software.
• Applying the workflow to a specific case study such as an earthquake in California, volcanic activity of Etna, or landslide event in Salzburg.
• Evaluating the results and discussing how the displacement maps could help in disaster response or risk management.

The purpose of this master thesis is to conduct a case study-based analysis of displacement mapping techniques, with detailed documentation in English. The thesis is ideal for students with a background in remote sensing, and an interest in geohazard applications.

Suggested reading

Teodosio, B., Wasantha, P. L. P., Yaghoubi, E., Guerrieri, M., Fragomeni, S., & van Staden, R. C. (2022). Monitoring of geohazards using differential interferometric satellite aperture radar in Australia. International Journal of Remote Sensing, 43(10), 3769–3802.

Start

As soon as possible

Prerequisites/qualification

Remote Sensing & GIS
Interest in the topic 

Live queries in the field: Bridging Earth Observation data and in-Situ Measurements

 Suggested by: Martin Sudmanns, Dirk Tiede

From big data to smartphone apps: Examples of Earth observation queries in the field.

Short description

Integrating Earth observation data with in-situ measurements for live analysis can be a powerful approach to enhance decision-making in agriculture, particularly within the framework of the Common Agricultural Policy (CAP). By enabling live queries in the field, accessing and analyzing EO data directly in real time become possible, supporting more accurate assessments of crop conditions, land use, and compliance with agricultural regulations.

This master thesis topic aims to explore the development of a seamless interface between EO data and in-situ measurements for agricultural applications. The objective is to create workflows that enable live queries in the field, utilizing EO datasets to provide actionable insights to stakeholders. Using a valuable tool such as Sen2Cube which is a semantically enriched data cube, the thesis will investigate how a mobile smartphone or tablet application can be used for live-interpretation of satellite imagery. The research outcomes are expected to improve the integration of EO data into agricultural monitoring systems, offering practical solutions for CAP-related assessments and supporting sustainable agricultural practices.

Suggested reading

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

As soon as possible

Prerequisites/qualification

++ interest in the topic
++ programming skills
+ Earth observation

Evaluation and visualization of long earth observation time series

 Suggested by: Martin Sudmanns, Dirk Tiede

Examples of long Earth observation time series. Visualization capabilities are usually on a lower level than the analytical capabilities. 

Short description

Long-term Earth observation data provide valuable insights into environmental changes, particularly in sensitive regions like the Alps. Visualizing and analysing these data dynamically can help better understand temporal trends and support environmental monitoring. For example, Essential Climate Variables (ECVs), such as vegetation indices or snow cover, are key indicators for assessing the impacts of climate change in mountainous regions and require data for multiple decades.

 The objective of this thesis is to evaluate and visualize long EO time series using the Sen2Cube.at which is a semantic data cube that enables the analysis of large volumes of EO. The work can be summarized into the following key points:

• Developing dynamic visualizations of long EO time series to highlight temporal trends in selected ECVs for the Alpine region.
• Using Sen2Cube.at queries to efficiently extract and analyze semantic data related to ECVs, such as vegetation health or snow cover dynamics.
• Demonstrating the applicability of the approach through case studies of key climate variables in specific Alpine areas.

This master thesis workflow will provide innovative tools for environmental monitoring, offering dynamic and accessible visualizations of climate-related changes that can be used to support decision-making in the Alpine region.

Suggested reading

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

As soon as possible

Prerequisites/qualification

++ interest in the topic
+ programming skills
+ Earth observation
+ Experience or willingness to get familiar with (3D) visualisation engines (Blender, unity, …)

Semi-Automated Delineation of Land Cover for Cartographic Use

 Suggested by: Johannes Scholz in cooperation with Dirk Tiede, Martin Sudmanns and the Österreichischer Alpenverein

Source: Österreichischer Alpenverein

Keywords: land cover delineation, cartographic representation, topographic maps

Objective: Create a reliable method to semi-automatically delineate land cover classes from base data for cartographic representation, reducing manual processing and enhancing map production quality.

Short Description: 

Geographic data is ubiquitous in the form of maps, though often presented poorly. To produce high-quality topographic maps, an exact delineation of land cover is required alongside many other processing steps. Distinguishing between forest and dwarf pines ("Latschen"), as well as between rocks and scree, still requires extensive manual work, especially when unsatisfied with poorly digitized OSM polygons. Reliably deriving these classes from available base data and preparing them for cartographic representation would add significant value to cartography.

Subsequent steps in map production, particularly the automated depiction of rocks and scree, rely on these delineations. A functional concept would also be welcomed by our partners, ensuring the practical application of the research results.

Start: Anytime

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