The aim is to develop methods for the largely automated generation of high-quality digital twins of existing infrastructure structures (e.g. roads, bridges, hydraulic engineering works) as a basis for operation and maintenance.
The sub-processes to be developed include the acquisition and processing of point clouds (e.g. by laser scanning or photogrammetry), extraction of geometric and semantic information using digital image processing, use of knowledge databases for semantic model generation and the evaluation of technical drawings using machine learning.
A major challenge is to extract the semantic information from the as-built documentation and the as-built survey and to enrich the digital twin. Furthermore, methods are to be applied to make the condition of a building assessable on the basis of images and point clouds.
Research focus of DC
In the course of the project, the chair takes on the responsibility to make Digital Twins available, linkable and analyzable.The cross-modal, multi-media linking of previously loosely structured data sets and information is carried out with methodical approaches and technical standards of Linked Data and Semantic Web Technologies.