The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-4/W4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 131–136, 2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 131–136, 2021

  07 Oct 2021

07 Oct 2021


A. Caselli, G. Falquet, and C. Métral A. Caselli et al.
  • Centre Universitaire d’Informatique (CUI), University of Geneva, Switzerland

Keywords: Subsurface objects, Knowledge graphs, Uncertain geometry, Temporal evolution, Collision detection

Abstract. In the recent years the concept of knowledge graph has emerged as a way to aggregate information from various sources without imposing too strict data modelling constraints. Several graph models have been proposed during the years, ranging from the “standard” RDF to more expressive ones, such as Neo4J and RDF-star. The adoption of knowledge graph has become established in several domains. It is for instance the case of the 3D geoinformation domain, where the adoption of semantic web technologies has led to several works in data integration and publishing. However, yet there is not a well-defined model or technique to represent 3D geoinformation including uncertainty and time variation in knowledge graphs. In this paper we propose a model to represent parameterized geometries of subsurface objects. The vocabulary of the model has been defined as an OWL ontology and it extends existing ontologies by adding classes and properties to represent the uncertainty and the spatio-temporal behaviour of a geometry, as well as additional attributes, such as the data provenance. The model has been validated on significant use cases showing different types of uncertainties on 3D subsurface objects. A possible implementation is also presented, using RDF-star for the data representation.