Open Theses – Institute of Cartography and Geoinformatics

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Open Bachelor Theses
Open Master Theses
Contact for general questions on theses

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Completed Theses

Open Bachelor Theses

Exploring Herrenhausen Gardens

Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience

Led by: Feuerhake, Sester

Year: 2023
Detection of Signatures in old Maps using Deep Learning

Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.

Led by: Thiemann, Sester

Year: 2023
Unveiling the Wireless Jungle

With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.

Led by: Schimansky, Golze

Year: 2023

Open Master Theses

Exploring Herrenhausen Gardens

Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience

Led by: Feuerhake, Sester

Year: 2023
Detection of Signatures in old Maps using Deep Learning

Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.

Led by: Thiemann, Sester

Year: 2023
Unveiling the Wireless Jungle

With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.

Led by: Schimansky, Golze

Year: 2023
Investigation of the Spatio-Temporal Impact of Traffic Accidents

Traffic accidents play an important role in our lives in terms of safety and security, especially for people. Everyone is affected by traffic accidents either directly (involved) or indirectly (consequences). Consequences such as traffic jams or road (lane) closures not only disrupt delivery and rush hour traffic, but can also lead to additional accidents. In addition, different types of traffic accidents can have different consequences. For example, an impact could be found in a reduction of the average travel speed on the road or on nearby roads in the time after an accident has occurred. The goal of this thesis is to investigate the impact of traffic accidents based on vehicle trajectories. Therefore, accident and trajectory data need to be linked and the spatial and temporal impact needs to be analyzed.

Led by: Golze

Year: 2023
Occupancy-free Space Modeling and Navigation Path Planning in a 3D Voxel Grid Environment for Urban Digital Twin Applications

The urban digital twin is an innovative concept within smart city technology, aiming to develop integrated and intelligent systems by harnessing diverse data from a multitude of sensors. Three-dimensional (3D) geodata plays a pivotal role in the representation and operation of urban digital twins. Tasks such as smart space management and navigation have become increasingly essential in urban digital twin applications, and they can be effectively facilitated using a foundation of 3D geospatial data. Therefore, this master thesis focuses on the modeling of unoccupied space and navigation path planning, employing a 3D voxel grid environment representation. The objective of the thesis is to develop a suitable approach for defining vacant space within urban area, which is utilized to enable collision-free 3D navigation. To achieve this, it is proposed to integrate the point cloud data of the Hannover urban area into a 3D voxel grid structure. In this context, grid cells containing point cloud data are treated as obstacles, while unoccupied cells are collectively constitute the occupancy-free space. The identified vacant space serve as a graph for implementing the shortest path algorithm. Ultimately, both the occupancy-free space and an illustrative route through it are visualized to demonstrate the approach viability.

Led by: Shkedova, Feuerhake

Team: Shkedova, Feuerhake

Year: 2023