The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 97–102, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-97-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 97–102, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-97-2021

  18 Nov 2021

18 Nov 2021

ESTABLISHING INDOOR SUBSPACING REQUIREMENTS OF AN LOD (LEVEL OF DETAIL) MODEL FOR GENERATING NETWORK-BASED TOPOLOGICAL DATA

A. R. C. Claridades1,2, H. S. Choi3, and J. Lee1 A. R. C. Claridades et al.
  • 1Dept. of Geoinformatics, University of Seoul, Dongdaemun-gu, Seoul, South Korea
  • 2Dept. of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
  • 3Korea Institute of Civil Engineering and Building Technology, Goyang-si, South Korea

Keywords: topological data model, subspacing, level of detail, indoor space

Abstract. Nowadays, the complexity of structures in urban environments and the interest in location-based applications increase simultaneously. Along with this is the rise in demand for the firm establishment of data models representing these spaces. Establishing network models that portray topological relationships of space have strengthened support for navigation applications. However, researchers have revisited the limitations of existing standards. As analogous standards have specifications for expressing space at various scales, most have focused on outdoor space or the geometric aspect. Hence, this paper proposes subspacing requirements for a Level of Detail (LOD) model for network-based topological data. We examine various constraints that influence space partition and align these with various application cases for indoor navigation. Through these, we investigate appropriate space subdivision approaches for each level according to applicable constraints and recommended applications. This study poses as an initial study towards establishing a general framework for implementing a 3D hierarchical network-based topological data model.