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A Comparison of Spatio-temporal Interpolation Methods
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
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With modern focus on LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. Furthermore, due to the popularity of LiDAR, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned terrain, in which case we obtain large spatio-temporal data sets. In dynamic terrains, such as coastal regions, such spatio-temporal data can offer interesting insight into how the terrain changes over time. An initial step in the analysis of such data is to create a digital elevation model representing the terrain over time. In the case of spatio-temporal data sets those models often represent elevation on a 3D volumetric grid. This involves interpolating the elevation of LiDAR points on these grid points. In this paper we show how to efficiently perform natural neighbor interpolation over a 3D volumetric grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory trade-offs. Our algorithm extends to higher dimensions. Our experimental results demonstrate that the algorithm is efficient and scalable. Categories and Subject.