Virtual pasture ABM

suggested by: Gudrun Wallentin

Background 

This research accompanies the revitalisation of a traditional alpine cattle pasture “Vierkaseralm” on Untersberg near Salzburg City. The study area was abandoned by the last farmer in 1958, since then shrubs (mainly Pinus mugo) have encroached the former grasslands. In summer 2021 the pasture was revived, the cattle got GPS devices and the area was scanned with a UAV. The data that have been collected since then provide a unique opportunity for scientific monitoring of pasture revitalisation.

Short description

This thesis aims to use agent-based simulation modelling to gain a better understanding of the cattle-grassland interaction dynamics following an alpine pasture revitalisation project. The available reference model will be modified, calibrated and validated with the cattles' GPS data and other available datasets in a pattern-oriented modelling approach, also using data-driven optimisation paradigms. Based on this reference model, the thesis will use scenario-based reserach to simulate alternative agro-ecological management strategies and / or test assumptions on cattle behaviour and vegetation dynamics. 

 

Suggested reading

Wallentin (2026) "Spatial Simulation" eBook

Start

anytime

Prerequsites/qualification
Interest in the topic. On-site involvement in the revitalisation activities is possible and highly welcome.

Utilizing Tracking Data to Create Defensive Performance Indicators for Football Players

Suggested by: Philip Schnittkamp, https://www.plaier.com/

Supervisor: Christian Neuwirth 

Short description: Football (or otherwise known as soccer) is one of the most complex sports to analyze with data. The low-scoring nature of the sport, as well as the fact that 22 players are active on a huge pitch at the same time, are just some of the reasons for the huge complexity of modeling team and player strengths. While the analysis of so-called event data has taken huge strides over the last years and is arguably especially helpful when analyzing the individual performance of attacking players, the rise of tracking data allows for much more complex spatio-temporal analysis on both the collective (team) and individual (player) level. Since then, public research has often concentrated on analyzing offensive patterns such as attacking pitch control, passing decision optimization, or off-ball runs to create space. Although there have also been major advances in analyzing defensive patterns like pressing efficacy or defensive compactness, there are still enough areas on the defensive side that remain un- or at least underexplored, especially on the individual player level.

The main idea of this master thesis is to further develop upon existing ideas and models to measure defensive player performance based on tracking data, specifically in terms of optimal defensive positioning to suppress the opposition’s threat. The utilization of several different statistical methods is conceivable to achieve this goal: already existing pitch control frameworks, neural nets, agent-based modelling approaches, etc. While the ultimate goal is to create a defensive performance measure on the individual player level, this is inevitably connected to the team level: Any good individual performance measure should, in the best case, lead to team success in the end to demonstrate its validity.

The following research questions should guide the analysis:

• Which players (and teams) are effective in hindering their opponents from spatio-temporally controlling the most dangerous areas of the pitch?
• What are the optimal defensive positions each player can take on to suppress the opposition’s threat based on their teammates’ and opponents’ positioning at any given time?
• Which players (and teams) are effective in taking on (close to) optimal positions on the defensive side? Which players (and teams) are effective in lowering opponent threat in comparison to expectation based on the opponent’s usual performances?
• Can a team’s defending style influence these results? And does it make sense to quantify this based on different areas of the pitch and/or different phases of defending?
• Are some players better at positioning themselves optimally in different areas of the pitch and/or different phases of defending?
• Are teams that are more effective in terms of defensive positioning better at hindering opponents from scoring goals? Are they more successful overall?

Start/finish: Anytime.

Prerequisites/qualifications: Experience with coding in Python (preferrable) or R is required. Personal interest and at least basic knowledge in football would be highly beneficial.

Suggested reading:

• Spearman, W. (2016, February). Quantifying pitch control. In OptaPro Analytics Forum [Internet]. https://doi.org/10.13140/RG.2.2.22551.93603.
• Fernández, Javier & Bornn, Luke. (2018). Wide Open Spaces: A statistical technique for measuring space creation in professional soccer.
• Higgins, Lewis & Galla, Tobias & Prestidge, Brian & Wyatt, Terry. (2023). Measuring the pitch control of professional football players using spatiotemporal tracking data. Journal of Physics: Complexity. 4. https://doi.org/10.1088/2632-072X/acb67d.
• Spearman, W. (2018, February). Beyond expected goals. In Proceedings of the 12th MIT sloan sports analytics conference (pp. 1-17).
• Peters, Andrew & Parmar, Nimai & Davies, Michael & James, Nic. (2026). Applying an Expected Pass Turnovers model to inform pressing strategies in professional football. International Journal of Performance Analysis in Sport. 1-16. https://doi.org/10.1080/24748668.2026.2671547.
• Forcher, L., Beckmann, T., Wohak, O., Romeike, C., Graf, F., & Altmann, S. (2024). Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI. Science and Medicine in Football, 8(4), 317–332. https://doi.org/10.1080/24733938.2023.2239766
• Forcher, L., Forcher, L., Altmann, S., Jekauc, D., & Kempe, M. (2024). Is a compact organization important for defensive success in elite soccer? – Analysis based on player tracking data. International Journal of Sports Science & Coaching, 19(2), 757-768.
• Forcher, L., Altmann, S., Forcher, L., Jekauc, D., & Kempe, M. (2022). The use of player tracking data to analyze defensive play in professional soccer - A scoping review. International Journal of Sports Science & Coaching, 17(6), 1567-1592.

 

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 

Spatial Performance Indicators for Parcel Pickup Stations

Suggested by: Martin Loidl

Short description: Parcel delivery systems increasingly rely on decentralised pickup stations whose performance and sustainability depend strongly on their spatial setting. Identifying suitable locations requires a detailed understanding of the spatial context and accessibility of potential locations.
This master thesis focuses on developing a spatial, data-driven approach to analyse and evaluate locations for parcel pickup stations. It includes compiling a comprehensive geodata inventory and applying accessibility modelling to assess site quality for walking, cycling, public transport, and motorised modes. Visual and analytic methods will be used to identify relationships between spatial structure, usage patterns, and CO2-reduction potential. The thesis may also involve developing or adapting automated GIS workflows for location scoring, or analysing the effect of different location types (e.g., residential areas, retail clusters, transportation hubs) on expected user behaviour.

By Matti Blume - Own work, CC BY-SA 4.0
https://commons.wikimedia.org/w/index.php?curid=69491925

Research for this master thesis should address one or more of the following questions (but is not limited to):

• Which spatial, infrastructural, and demographic characteristics define high-quality locations for parcel pickup stations?
• How can multimodal accessibility be modelled to support objective and comparable assessment of potential locations?
• How do different location types influence usage patterns and the potential reduction of motorised delivery trips?

References, suggested reading:

• PRANDTSTETTER, M., SERAGIOTTO, C., BRAITH, J., EITLER, S., ENNSER, B., HAUGER, G., HOHENECKER, N., SCHODL, R. & STEINBAUER, M. 2021. On the Impact of Open Parcel Lockers on Traffic. Sustainability, 13, 755. doi:10.3390/su13020755 
• NIEMEIJER, R. & BUIJS, P. 2023. A greener last mile: Analyzing the carbon emission impact of pickup points in last-mile parcel delivery. Renewable and Sustainable Energy Reviews, 186, 113630. doi:10.1016/j.rser.2023.113630 
• VAN DER MEER, L., WERNER, C. & LOIDL, M. 2024. Assessment of bicycle accessibility to mobility hubs under different criteria for cycling network quality. AGILE GIScience Ser., 5, 48. doi: 10.5194/agile-giss-5-48-2024
• OZYAVAS, P., BUIJS, P., URSAVAS, E. & TEUNTER, R. 2025. Designing a sustainable delivery network with parcel locker systems as collection and transfer points. Omega, 131, 103199. doi:10.1016/j.omega.2024.103199

Start/finish: anytime

Prerequisites/qualifications: Interest in spatial analysis, mobility behaviour, and data-driven urban logistics. Experience with GIS and data management is required; scripting skills (Python, R) are beneficial for automated workflows.

Spatial Group Model Building for Investigating Mobility Trends

Suggested by: Martin Loidl

Short description: Understanding mobility-related impacts requires modelling complex interactions between behaviour, infrastructure, and spatial context. Spatial Group Model Building (SGMB) offers a participatory method to conceptualize such systems by integrating stakeholder knowledge with spatial reasoning.
This master thesis investigates the suitability of SGMB for developing spatially explicit conceptual models, using the uptake of e-bikes among kids and young adolescents as a concrete use case. The work includes designing and applying an SGMB workflow, developing spatial causal diagrams, assessing methodological strengths and limitations, and exploring how SGMB outputs can inform subsequent quantitative analyses of mobility, safety, accessibility, or environmental impacts.

 Research for this master thesis should address one or more of the following questions (but is not limited to):

• How can SGMB support the development of spatially explicit conceptual models related to increasing e-bike use among kids and young adolescents?
• Which spatial, social, and infrastructural factors most strongly influence the likelihood that young adolescents adopt e-bikes, and how can these relationships be captured through Spatial Group Model Building?
• How do perceived risks (e.g., safety concerns), access to suitable infrastructure, and everyday mobility needs interact to shape e-bike uptake among kids and young adolescents in different spatial contexts?
• Which feedback mechanisms, such as changes in independent mobility, social norms, or exposure to traffic environments, drive or hinder the spread of e-bike use among adolescents, and how can SGMB help identify them across regions or settlement types? 

Suggested reading:

• SCOTT, R. J., CAVANA, R. Y. & CAMERON, D. 2016. Recent evidence on the effectiveness of group model building. European Journal of Operational Research, 249, 908-918. doi:10.1016/j.ejor.2015.06.078
• WEIR, H., BRENDAN, M., IRAKLIS, A., CLAIRE, C., CONOR, M., JOHN, B., ALBERTO, L., GARY, M., FRANK, K., RUTH, H. & AND GARCIA, L. 2024. Group model building for developing systems-oriented solutions to reduce car dependency in Belfast, United Kingdom. Cities & Health, 8, 374-389. doi:10.1080/23748834.2024.2328952
• Principles of group model building and spatial group model building: Slideshare 

Related project: This thesis can be linked to the i-MOBYL project. More information is available on the research group’s website.

Start/finish: anytime

Prerequisites/qualifications: Interest in mobility and transport research, participatory modelling, and spatial systems analysis. Experience with GIS is required; qualitative or conceptual modelling skills are an advantage.

 

Validating OpenStreetMap Data for Public Transport Stops Using Street-Level Imagery

Suggested by: Martin Loidl

Short description: Public transport (PT) operators and public authorities typically provide detailed location and timetable information on PT stops. However, data on the physical design, equipment, and immediate surroundings of these stops is often incomplete or entirely missing. This lack of information limits analyses on topics of high practical relevance, such as accessibility for users with disabilities, comfort and safety at stops, or environmental exposure (e.g., shade or sun).
OpenStreetMap (OSM) is widely recognized as a valuable source of transport-related geodata, offering a rich set of tags that describe physical features of PT stops. Yet, the completeness and accuracy of these attributes remain uncertain, particularly in rural areas.
This master thesis focuses on validating OSM data for public transport stops in rural contexts. It involves evaluating the accuracy and completeness of selected stop attributes (e.g., shelter, seating, signage, lighting) by systematically comparing OSM entries with street-level imagery from platforms such as Mapillary or Google Street View. The work includes developing a sampling strategy for rural stops, collecting and analyzing visual evidence, and documenting deviations between mapped information and observed reality. The goal is to assess OSM’s data quality for rural PT infrastructure and identify systematic patterns in missing or incorrect attributes.

The following research questions can guide the analysis:

• Which attributes of public transport stops are most frequently missing or incorrect in OSM?
• How reliable is street-level imagery for validating rural transport infrastructure?
• What sampling approach ensures representative coverage of rural stops?
• How can findings inform strategies for improving OSM data quality?

Related project: This thesis contributes to the SAFARI project, which focuses on identifying mobility barriers for vulnerable population groups. More information is available on the research group’s website.

Start/finish: anytime

Prerequisites/qualifications: Interest in mobility and transport planning, as well as in spatial data analysis. Skills in data management and geospatial analysis are required; scripting and coding skills are advantageous.