Progress in Spatial Data Handling pp 771-788 | Cite as

# From Point Cloud to Grid DEM: A Scalable Approach

## Abstract

Given a set S of points in ℝ^{3} sampled from an *elevation* function *H* : ℝ^{2} → ℝ, we present a scalable algorithm for constructing a grid digital elevation model (DEM). Our algorithm consists of three stages: First, we construct a quad tree on S to partition the point set into a set of non-overlapping segments. Next, for each segment *q*, we compute the set of points in *q* and all segments neighboring *q*. Finally, we interpolate each segment independently using points within the segment and its neighboring segments.

Data sets acquired by LIDAR and other modern mapping technologies consist of hundreds of millions of points and are too large to fit in main memory. When processing such massive data sets, the transfer of data between disk and main memory (also called I/O), rather than the CPU time, becomes the performance bottleneck. We therefore present an *I/O-efficient* algorithm for constructing a grid DEM. Our experiments show that the algorithm scales to data sets much larger than the size of main memory, while existing algorithms do not scale. For example, using a machine with 1GB RAM, we were able to construct a grid DEM containing 1.3 billion cells (occupying 1.2GB) from a LIDAR data set of over 390 million points (occupying 20GB) in about 53 hours. Neither ArcGIS nor GRASS, two popular GIS products, were able to process this data set.

## Keywords

Grid Cell Point Cloud Main Memory Input Point Internal Memory## Preview

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