Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.
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.
Nous tenons à remercier les doctorants, post-doctorants, ingénieurs et stagiaires qui ont contribué au développement du cadre UD-SV, au cours des dernières années, au sein du laboratoire LIRIS dans le cadre du projet VCity. Ce travail est, en partie, soutenu par plusieurs entreprises (Oslandia, Berger-Levrault, IGO). Il a également été partiellement soutenu par le LABEX IMU (ANR-10-LABX-0088) de l'Université de Lyon, dans le cadre du programme « Investissements d'avenir » (ANR-11-IDEX-0007) et le programme DatAgora supporté par l'IDEX
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