Natural neighbor interpolation based grid DEM construction using a GPU

Abstract

With modern LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. One of the most fundamental GIS tasks is to construct a grid digital elevation model (DEM) from these 3D point clouds. In this paper we present a simple yet very fast algorithm for constructing a grid DEM from massive point clouds using natural neighbor interpolation (NNI). We use a graphics processing unit (GPU) to significantly speed up the computation. To handle the large data sets and to deal with graphics hardware limitations clever blocking schemes are used to partition the point cloud. For example, using standard desktop computers and graphics hardware, we construct a high-resolution grid with 150 million cells from two billion points in less than thirty-seven minutes. This is about one-tenth of the time required for the same computer to perform a standard linear interpolation, which produces a much less smooth surface.

Performance improvement of viewshed analysis using GPU

“…According to preliminary results, GPU algorithm decreased processing time by 900%. Beutel et al in [5] considered construction of a grid digital elevation model (DEM) from 3D point clouds generated by the LiDAR equipment. The obtained results showed that using of a graphics processing unit (GPU) for this type of GIS application can significantly speed up the computation.…”

Speeding up the high-accuracy surface modelling method with GPU

“…A handful of works has been done related to spatial data interpolation and DEM construction. Beutel et al (2010) realized a GPU-based natural neighbour interpolation algorithm to generate DEM and accomplished a 10 times speedup ratio. Xia et al (2011) implemented IDW algorithm on a GPU and made a range of speedups from 12 to 33 depending on the scale and resolution of dataset.…”

Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

“…This massive set of points is called "point cloud." Modern airborne LiDAR technology can map the Earth's surface at a 15-20-cm horizontal resolution, and future generations of LiDAR scanners are expected to generate even higher resolution maps [1]. The large volume of scanned data that are manipulated when processing a LiDAR point cloud has been one of the major challenges in data processing.…”

“…Han et al [18] use a PC cluster and a virtual grid to create a raster DSM and then produce a digital terrain model using enormous amounts of airborne laser scanning data. Beutel et al [1] construct a grid DEM from massive point clouds using natural neighbor interpolation and get a 10× increase in speed using a graphics processing unit (GPU). Fieldprogrammable gate array (FPGA) [19] and cloud computing [20] are also used to improve processing efficiency.…”