Influence of Urban Structures on Perception and Wayfinding – Experiment in VR

Built and natural structures have a decisive impact on how people perceive their surroundings and navigate within them. Elements such as the structure of the street network (e.g., grid-based city layouts versus organically grown European cities), building density, and the presence of open and green spaces strongly affect our ability to orient ourselves. Distinct landmarks play a crucial role as mental anchor points along visual axes. In addition, traffic density and traffic modes also shape the perception of urban environments.

Virtual environments provide the opportunity to systematically vary these factors and study their effects in isolation. The aim of this master’s thesis is to experimentally analyze key aspects of urban design—such as street networks, building density, open and green spaces, and traffic—and to measure their influence on perception, orientation, and wayfinding. To achieve this, different virtual environments will be created, navigation tasks developed, and systematic experiments with participants conducted. The results will not only contribute to a deeper understanding of human spatial perception but also provide valuable insights for future urban planning and design.

Digital Twin for Sensor-Based Data Collection and Prediction

As part of this project, a digital twin of the Geo1 building will be developed based on an existing 3D model. The digital twin will be linked to real sensor data (e.g., CO₂ concentration, temperature, humidity) collected in selected rooms such as seminar and teaching rooms. The goal is to integrate the collected data into the digital twin and to develop models that can generate predictions of CO₂ levels and other indoor climate parameters under different usage scenarios.

The project includes the technical implementation of the digital twin, the connection and integration of sensor data, the design of appropriate scenarios (e.g., varying occupancy levels, ventilation strategies), as well as the development and validation of predictive models.

TinyAIoT: Ressource-efficient AI Models for IoT Sensors

Modern IoT applications often rely on sensors that run on microcontroller units and communicate via network protocols such as LoRaWAN or Bluetooth Low Energy. To operate autonomously for extended periods of time, application resource requirements must be minimized.

This master’s thesis investigates the development of resource-efficient IoT applications through the utilization of AI models. These models aim to save energy by reducing the computational load, camera resolution or data transmission, while maintaining the ability to perform specific tasks. You will develop, implement and compare different resource-efficient AI models with sensors such as cameras, distance sensors, vibration sensors and test your implementation in different application scenarios.

Fostering Navigational Map Reading Competence

The ability to orient oneself and read maps is essential to successfully navigate in unfamiliar environments. It is well known that the ability to orient oneself with maps varies from person to person. While there are numerous navigation systems to help us find our way, very few efforts have been made to use GI technologies to promote orientation and map reading skills and overcome the individual differences. GeoGami is a location-based game using digital maps to systematically teach navigational map reading competence.

The thesis will investigate how to design trainings to promote people’s navigational map reading competence with digital maps. Successful map reading relies on the ability to localize yourself on the map, to localize objects on the map and to align the map with your environment. You will design tasks to practise these competences at different levels of difficulty. These trainings can be either conducted in the real world or in virtual worlds explicitly designed for these trainings. You will run a study testing your trainings to identify the most efficient ways to teach navigational map reading.

Literature:

Lobben, A. K. (2007). Navigational map reading: Predicting performance and   identifying relative influence of map-related abilities. Annals of the Association of American Geographers, 97(1), 64–85. https://doi.org/10.1111/j.1467-8306.2007.00524.x

J Bistron, A Schwering (2023): Assessing navigational map reading competencies with the location-based GeoGame “GeoGami”, Journal of Geoscience education 72 (1), 73-85, https://doi.org/10.1080/10899995.2023.2190830

Interpreting Spatial Movement Behaviour

Every day, humans move through space and time. Traditionally, performance in navigation tasks has been assessed primarily by measuring the time or distance required to reach a destination. With the use of mobile sensors such as GPS and digital compasses, it is now possible to record spatio-temporal movement data in great detail and even capture contextual information about the surrounding environment. However, such spatio-temporal trajectories alone do not provide direct insights into actual movement behaviour. For example: does a decrease in speed indicate disorientation, or is it simply a reaction to environmental factors? Conversely, does high speed reflect confidence in wayfinding?

In this thesis, you will investigate spatial trajectories of participants performing wayfinding tasks. Your objectives will be to identify suitable measures for analysing and interpreting trajectories – e.g. how to determine wayfinding confidence and getting lost based on speed, viewing direction and trajectory – , to develop algorithms that extract relevant information from trajectory data, and to test your approach using existing datasets.

You will have access to trajectories collected during wayfinding studies with our research software GeoGami (www.geogami.org respectively https://app.geogami.ifgi.de/)

Measures for wayfinding performance measures (not technology supported):

Hölscher et al (2006): Up the down staircase: Wayfinding strategies in multi-level buildings, https://doi.org/10.1016/j.jenvp.2006.09.002

Ruddle et al (2006): Three Levels of Metric for Evaluating Wayfinding, https://doi.org/10.1162/pres.15.6.637

SketchMapia – A Research Software to Assess Human Spatial Knowledge

Sketch mapping, i.e. freehand drawings of maps on a sheet of paper, is a popular and powerful method to explore a person’s spatial knowledge. Although sketch maps convey rich spatial information, such as the spatial arrangement of places, buildings, streets etc., the methods to analyse sketch maps are extremely simple. At the spatial intelligence lab, we developed a software suite, called SketchMapia, that supports the systematic and comprehensive analysis of sketch maps in experiments.

In this master thesis, you develop systematic test data for a sketch map analysis method and evaluate the SketchMapia analysis method w.r.t. its compleness, correctness and performance against other sketch map analysis methods. 

Analysis of Spatiotemporal Movement Patterns in Multi-Player Geogames

In multi-player geogames, players collaborate to solve wayfinding tasks and thereby form groups. The trajectories of these groups can be analyzed using established concepts from the literature (e.g., Gudmundsson, van Kreveld & Speckmann), which distinguish characteristic motion patterns such as Flock (common direction and proximity), Leadership (a subgroup follows a “leader”), Convergence (movement towards the same location), and Encounter (meeting at the same place and time).

At the Institute for Geoinformatics, the location-based game GeoGami (www.geogami.org / https://app.geogami.ifgi.de/ provides the technical framework for multiplayer games and the collection of movement data. Games can be implemented flexibly in real-world outdoor and indoor settings as well as in virtual reality environments, thus generating diverse trajectory datasets.

In this thesis, you will develop and apply methods for comparing group trajectories with respect to similarities in direction, speed, position, and spatial proximity of trajectory points over time intervals. These methods are to be implemented and evaluated using empirical trajectory data from multiplayer geogames.

Gudmundsson, J., van Kreveld, M. & Speckmann, B. Efficient Detection of Patterns in 2D Trajectories of Moving Points. Geoinformatica 11, 195–215 (2007). https://doi.org/10.1007/s10707-006-0002-z

Gudmundsson, J., Laube, P., Wolle, T. (2008). Movement Patterns in Spatio‐temporal Data. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. , pp 726–732, https://doi.org/10.1007/978-0-387-35973-1_823