TerraNNI

Abstract

With modern focus on remote sensing technology, such as LiDAR, the amount of spatial data, in the form of massive point clouds, has increased dramatically. Furthermore, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned areas, in which case we obtain large spatiotemporal datasets. An initial step in the analysis of such spatial data is to create a digital elevation model representing the terrain, possibly over time. In the case of spatial (spatiotemporal, respectively) datasets, these models often represent elevation on a 2D (3D, respectively) grid. This involves interpolating the elevation of LiDAR points on these grid points. In this article, we show how to efficiently perform natural neighbor interpolation over a 2D and 3D grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory tradeoffs. Our experimental results demonstrate that our algorithms not only are significantly faster than earlier ones but also scale to much bigger datasets that previous algorithms were unable to handle.

Terrain trees: a framework for representing, analyzing and visualizing triangulated terrains

“…Let t be a triangle and p i , p j , p k the vectors of coordinates of its three vertices. If t is nonobtuse, the contribution of t to the mixed area is 1 8…”

“…A raster representation is often used for modeling a terrain or a surface from airborne LiDAR data, mainly because of the availability of a large number of software tools for processing raster data in Geographic Information Systems (GISs) and remote sensing. While it is always possible to transform a point cloud into a raster representation, this is a computationally intensive operation, which can account for 70-80% of the time required by the total analysis pipeline [6,1]. Also, artifacts might be created due to the resolution of the raster grid, especially if the original point cloud contains noise due to acquisition errors, or if it contains missing data, due to the presence of occlusions.…”

“…But compact and widely-used data structures for encoding TINs suffer from scalability issues. For instance, using the most compact state-of-art data structure for triangle meshes, we can process meshes up to 150 million of vertices (300 million of triangles) on our workstation with an Intel Xeon E5-2630 v4 CPU at 2.20Ghz and 64GB of RAM, which is much lower than the size of currently available data sets (see, for instance, the OpenTopography repository 1 ).…”