Download
Publications Copernicus
Download
Citation
Articles | Volume XLVI-4/W4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 43–48, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-43-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4-2021, 43–48, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-43-2021

  07 Oct 2021

07 Oct 2021

ADDRESSING THE ELEPHANT IN THE UNDERGROUND: AN ARGUMENT FOR THE INTEGRATION OF HETEROGENEOUS DATA SOURCES FOR RECONCILIATION OF SUBSURFACE UTILITY DATA

L. H. Hansen1, R. van Son2, A. Wieser3, and E. Kjems1 L. H. Hansen et al.
  • 1Department of the Built Environment, Aalborg University, Denmark
  • 2Singapore-ETH Centre, Singapore
  • 3Institute of Geodesy and Photogrammetry, ETH Zürich, Switzerland

Keywords: underground infrastructure modelling, geospatial data integration, data fusion, data quality

Abstract. In this paper we address the issue of unreliable subsurface utility information. Data on subsurface utilities are often positionally inaccurate, not up to date, and incomplete, leading to increased uncertainty, costs, and delays incurred in underground-related projects. Despite opportunities for improvement, the quality of legacy data remains unaddressed. We address the legacy data issue by making an argument for an approach towards subsurface utility data reconciliation that relies on the integration of heterogeneous data sources. These data sources can be collected at opportunities that occur throughout the life cycle of subsurface utilities and include as-built GIS records, GPR scans, and open excavation 3D scans. By integrating legacy data with newly captured data sources, it is possible to verify, (re)classify and update the data and improve it for future use. To demonstrate the potential of an integration-driven data reconciliation approach, we present real-world use cases from Denmark and Singapore. From these cases, challenges towards implementation of the approach were identified that include a lack of technological readiness, a lack of incentive to capture and share the data, increased cost, and data sharing concerns. Future research should investigate in detail how various data sources lead to improved data quality, develop a data model that brings together all necessary data sources for integration, and a framework for governance and master data management to ensure roles and responsibilities can be feasibly enacted.