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.

 

Modeling Place Vulnerability to Explosive Disease Outbreaks

Suggested by: Christian Neuwirth (Z_GIS – Spatial Simulation)
 
Short description:

In addition to the basic reproduction number, R0, the overdispersion parameter, k, plays a crucial role in characterizing the spread of infectious diseases. Estimates for COVID-19 indicate that the dispersion parameter k is approximately 0.1, suggesting that 80% of transmissions have been caused by only 10% of infectious individuals [1]. 

Observed overdispersion can arise from various factors. For instance, the same pathogen may exhibit different behaviors across individuals, e.g. the infectious period is better represented as a distribution rather than a fixed constant [2]. 

Additionally, observed overdispersion in disease transmission may stem from overdispersion in social contact networks. For example, a French social contact survey caried out by [3] demonstrated that a small number of individuals account for a disproportionately large share of overall social contacts, while many individuals have few or no social interactions.
Modeling experiments indicate that outbreaks within such social networks tend to be particularly explosive (Fig. 1).

 
Figure 1. The blue curves represent simulated outbreaks in empirical social networks exhibiting overdispersion, while the red curves depict outbreaks in networks where every individual has an equal number of social contacts. The basic reproduction numbers are as follows: R0=1.8 (A), R0=2.5 (B), R0=3.1 (C), and R0=3.7 (D).
 

Hypothesis: It is hypothesized that overdispersion in social contact networks is influenced by the physical structures of space, such as transportation infrastructure and other elements of the built environment. For instance, recent investigations showed that hierarchical cities are more vulnerable to the rapid spread of infectious diseases than decentralized cities [4]. In other words, overdispersion in physical structures translates into overdispersion in social contact networks, which in turn leads to overdispersion in disease transmission and explosive outbreaks.

The aim of this thesis is to quantify the vulnerability of locations to epidemic outbreaks by analyzing their structural properties.

Method: (1) Quantify the overdispersion parameter k in physical infrastructures using data from OpenStreetMap or open air travel network data (with the appropriate scale to be determined), (2) Run network simulations in a SIR-model (model is available) using the empirical parameter k as an input, (3) Compare epidemic doubling time in the simulation with empirical COVID-19 excess mortality doubling time at selected sites using a ranking scale approach.

Start: ASAP

Prerequisites/qualifications: 
Interest in spatial simulation and scripting (NetLogo, Python, R or GAMA)

Please contact Christian Neuwirth in case of interest: christian.neuwirth@plus.ac.at

References:

1. K. Sneppen, B. F. Nielsen, R. J. Taylor, and L. Simonsen, “Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control,” Proceedings of the National Academy of Sciences, vol. 118, no. 14, p. e2016623118, 2021.
2. A. L. Lloyd, “Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods,” Proceedings of the Royal Society of London. Series B: Biological Sciences, vol. 268, no. 1470, pp. 985–993, 2001.
3. G. Béraud et al., “The French connection: the first large population-based contact survey in France relevant for the spread of infectious diseases,” PloS one, vol. 10, no. 7, p. e0133203, 2015.
4. J. Aguilar et al., “Impact of urban structure on infectious disease spreading,” Scientific reports, vol. 12, no. 1, p. 3816, 2022.
5. O. Wegehaupt, A. Endo, and A. Vassall, “Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature,” BMC Public Health, vol. 23, no. 1, p. 1003, 2023.

Smallholder Farming and Global Crop Masks

Suggested by: Sophia Klaußner, Lorenz Wendt (CDL GEOHUM)

Short description: 
Studies have shown that landcover models are not sufficient in correctly identifying crop land in Sub-Saharan Africa. This has grave implications as early warning systems and the distribution of support in case of emergencies therefore gets significantly delayed.

In this study the student will investigate an area with small-holder farming to evaluate the accuracy of global models for small-holders and develop usability guidance from this. They will create a validation dataset and evaluate several global models for performance with small-scale agriculture and explore the implications related to this. Further they will create a help for deciding what land cover model to use in different contexts.

Suggested Reading:

• Dlamini, L., Crespo, O., Van Dam, J., & Kooistra, L. (2023). A global systematic review of improving crop model estimations by assimilating remote sensing data: Implications for small-scale agricultural systems. Remote Sensing, 15(16), 4066. https://doi.org/10.3390/rs15164066
• Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., & Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? Scientific Data, 11(1), 486. https://doi.org/10.1038/s41597-024-03306-z
• Ketema, H., Wei, W., Legesse, A., Wolde, Z., Temesgen, H., Yimer, F., & Mamo, A. (2020). Quantifying smallholder farmers’ managed land use/land cover dynamics and its drivers in contrasting agro-ecological zones of the East African Rift. Global Ecology and Conservation, 21, e00898. https://doi.org/10.1016/j.gecco.2019.e00898