BAM - Big-Data-Analytics in Environmental and Structural Monitoring

Flowchart of the project Vanessa Liebler for the i3mainz, CC BY SA 4.0

An interdisciplinary team at Mainz University of Applied Sciences is exploring the potentials of current data mining and machine learning methods for research questions with a space-time relevance in the context of big data.


The aim of the research project is to provide methods that increase the benefit of rapidly growing data volumes with a spatial bearing. In the area of smart cities, for example, information systems based on machine learning are being developed that facilitate decision-making through analysis and visualization. Furthermore, the degree of autonomy of optical monitoring systems based on image analysis is being increased by using deep learning systems.


The exchanging of interests with various project partners from the mobility sector enabled the discovery of overlaps, the development of use cases and the pooling of potentially promising data. Working on this basis, Alexander Rolwes and Thomas Müller are implementing a prototype application for predicting parking garage utilization in Mainz. At present, they are dedicated to the continued development of the predictive system and its integration into a comprehensive guidance model for the use of off-street parking spaces. They are also investigating the relationships between spatial influencing factors and parking behavior.

Kira Zschiesche and Denise Becker are implementing promising methods for monitoring changes in building structures. For example, they are acquiring insights in connection with methods from structural health monitoring by performing optical vibration measurements on building structures and testing the first approaches for automatic crack detection. They are also developing a software solution to improve railroad track safety using a deep learning approach that identifies trains in images using artificial intelligence. In the future, the team will explore methods for automatic target detection to increase the degree of automation in various processes.