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
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
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 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.
Conference paper (PDF, 2949 KB)


Citation: Psomadaki, S., van Oosterom, P. J. M., Tijssen, T. P. M., and Baart, F.: USING A SPACE FILLING CURVE APPROACH FOR THE MANAGEMENT OF DYNAMIC POINT CLOUDS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 107-118, https://doi.org/10.5194/isprs-annals-IV-2-W1-107-2016, 2016.

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