Leveraging Standards in Model-Centric Geospatial Knowledge Graph Creation

Abstract

Understanding the complex urban landscapes of cities and their evolution is becoming an ever more essential area of research for urbanists, city planners, historians, and industry leaders. Toward this endeavor, data-driven 3D semantic city models can be implemented to create tools for understanding, simulating, and modeling these urbanization processes and many other urban phenomena. These implementations often require integrating multidimensional (2D/3D, temporal, and thematic), heterogeneous, and multisource urban data to provide users with more complete views of the changing urban landscape. In recent years, researchers have turned toward Semantic Web technologies such as knowledge graphs as common platforms for integrating these data and their underlying data models. However, simple transformation or conversion of urban data towards these formats is prone to data loss, and integration of urban data model standards lacking interoperability poses its own challenges. This work proposes a model-centric urban data transformation approach towards Semantic Web data formats, based on international standards for facilitating the integration of these urban data and promoting their interoperability in the context of multidimensional city modeling.

Representation of Urban Geometry Evolution Through Space-Time Cube

“…For example, the transaction between State 1 and State 2 is a modification. We rely on the work from Vinasco-Alvarez [12] and use Resource Description Framework (RDF) knowledge graphs to structure the different versions of our data. This structure allows to link a transaction to a version of the data in the graph and to retrieve all versions of a given city object as well as its changes.…”