ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume VIII-4/W2-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W2-2021, 37–44, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-37-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W2-2021, 37–44, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-37-2021

  07 Oct 2021

07 Oct 2021

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

D. Vinasco-Alvarez1, J. Samuel2, S. Servigne3, and G. Gesquière1 D. Vinasco-Alvarez et al.
  • 1Université Lumière Lyon 2, LIRIS UMR-CNRS 5205, Université de Lyon, Lyon, France
  • 2CPE Lyon, LIRIS, UMR-CNRS 5205, Université de Lyon, Lyon, France
  • 3INSA Lyon, LIRIS UMR-CNRS 5205, Université de Lyon, Lyon, France

Keywords: Graphs, Networks of Ontologies, 3D Urban Data, Spatio-temporal Data, Data Integration, Data Transformation, CityGML, GeoSPARQL

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.