Comparison of cloud-to-cloud distance calculation methods for change detection in spatio-temporal point clouds
- 1Delft University of Technology, Faculty of Architecture and the Built Environment, GIS technology, Delft, Netherlands (v.diazmercado@tudelft.nl)
- 2Delft University of Technology, Faculty of Architecture and the Built Environment, GIS technology, Delft, Netherlands
- 3Delft University of Technology, Faculty of Architecture and the Built Environment, GIS technology, Delft, Netherlands
- 4Delft University of Technology, Faculty of Architecture and the Built Environment, GIS technology, Delft, Netherlands
- 5Netherlands eScience Center, Amsterdam, Netherlands
- 6Netherlands eScience Center, Amsterdam, Netherlands
The advantages of using point clouds for change detection analysis include comprehensive spatial and temporal representation, as well as high precision and accuracy in the calculations. These benefits make point clouds a powerful data type for spatio-temporal analysis. Nevertheless, most current change detection methods have been specifically designed and utilized for raster data. This research aims to identify the most suitable cloud-to-cloud (c2c) distance calculation algorithm for further implementation in change detection for spatio-temporal point clouds. Eight different methods, varying in complexity and execution time, are compared without converting the point cloud data into rasters. Hourly point cloud data from monitoring a beach-dune system's dynamics is used to carry out the comparison. The c2c distance methods are (1) the nearest neighbor, (2) least squares plane, (3) linear interpolation, (4) quadratic (height function), (5) 2.5D triangulation, (6) natural neighbor interpolation (NNI), (7) inverse distance weight (IDW) and (8) multiscale model to model cloud comparison (M3C2). We evaluate these algorithms, considering both the accuracy of the calculated distance and the execution time. The results can be valuable for analyzing and monitoring the (build) environment with spatio-temporal point cloud data.
Key terms: point cloud, spatio-temporal analysis, c2c distance, beach-dune system
References
Van Oosterom, P., van Oosterom, S., Liu, H., Thompson, R., Meijers, M. and Verbree, E. Organizing and visualizing point clouds with continuous levels of detail. ISPRS J. Photogramm. Remote Sens. 194 (2022) 119. https://doi.org/10.1016/J.ISPRSJPRS.2022.10.004
Vos, S., Anders, K., Kuschnerus, M., Lindenbergh, R., Höfle, B., Aarninkhof, S. and de Vries, S. A high-resolution 4D terrestrial laser scan dataset of the Kijkduin beach-dune system, The Netherlands. Sci Data 9, 191 (2022). https://doi-org.tudelft.idm.oclc.org/10.1038/s41597-022-01291-9
How to cite: Diaz, V., van Oosterom, P., Meijers, M., Verbree, E., Ahmed, N., and van Lankveld, T.: Comparison of cloud-to-cloud distance calculation methods for change detection in spatio-temporal point clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8191, https://doi.org/10.5194/egusphere-egu24-8191, 2024.
