Identification and Interpretation of Change Patterns in Semantic 3D City Models

Urban Digital Twins have received significant attention in recent years due to their economic and research importance. Although many definitions exist, the general consensus agrees on a continuous two-way data flow between a physical entity and its virtual counterpart in a digital twin. In the context of smart cities and semantic 3D city models, however, no major breakthrough in realizing such complex change detection and analysis systems has yet been achieved. While several methods for change detection in semantic 3D city models have been proposed, the analysis of found changes, especially the identification of patterns among a large number of changes, has not been given as much attention. Without a proper handling of patterns, it is difficult to provide useful interpretation of changes with respect to stakeholders. Therefore, this research proposes a framework to define, detect and decipher complex semantic change patterns in semantic 3D city models. The approach provides a central rule network to describe aggregation relations between changes as well as methods to identify and capture detected change patterns directly in the graph representation of a city model.