TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION

Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area research in enrichment 3D 4D (3D + Time) semantic models. These models, which can represent geospatial their evolving relations, may require data-driven approaches to provide temporal concurrent views landscape. However, often requires transformation or conversion into single shared format, be prone loss. combat this, this paper proposes model-centric ontology-based approach towards limiting loss transformations graph formats. By integrating underlying conceptual models standards, unified model created as network ontologies. Transformation tools use map datasets interoperable formats This will firstly illustrate how facilitates rich geospatial, web standards with focus on Secondly, demonstrate graphs based these implemented for spatial queries toward enrichment.