ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 107–118, 2016
https://doi.org/10.5194/isprs-annals-IV-2-W1-107-2016
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 107–118, 2016
https://doi.org/10.5194/isprs-annals-IV-2-W1-107-2016

  05 Oct 2016

05 Oct 2016

USING A SPACE FILLING CURVE APPROACH FOR THE MANAGEMENT OF DYNAMIC POINT CLOUDS

S. Psomadaki1, P. J. M. van Oosterom1, T. P. M. Tijssen1, and F. Baart2 S. Psomadaki et al.
  • 1TU Delft, Faculty of Architecture and the Built Environment, Department OTB, 2600 GA Delft, the Netherlands
  • 2Deltares, 2600 MH, Delft, the Netherlands

Keywords: Point cloud data, Space filling curve, Spatio-temporal data, Benchmark, DBMS

Abstract. Point cloud usage has increased over the years. The development of low-cost sensors makes it now possible to acquire frequent point cloud measurements on a short time period (day, hour, second). Based on the requirements coming from the coastal monitoring domain, we have developed, implemented and benchmarked a spatio-temporal point cloud data management solution. For this reason, we make use of the flat model approach (one point per row) in an Index Organised Table within a RDBMS and an improved spatio-temporal organisation using a Space Filling Curve approach. Two variants coming from two extremes of the space–time continuum are also taken into account, along with two treatments of the z dimension: as attribute or as part of the space filling curve. Through executing a benchmark we elaborate on the performance – loading and querying time –, and storage required by those different approaches. Finally, we validate the correctness and suitability of our method, through an out-of-the-box way of managing dynamic point clouds.