We consider the problem of extracting a river network and a watershed hierarchy from a terrain given as a set of irregularly spaced points. We describe TerraStream, a “pipelined ” solution that consists of four main stages: construction of a digital elevation model (DEM), hydrological conditioning, extraction of river networks, and construction of a watershed hierarchy. Our approach has several advantages over existing methods. First, we design and implement the pipeline so each stage is scalable to massive data sets; a single non-scalable stage would create a bottleneck and limit overall scalability. Second, we develop the algorithms in a general framework so that they work for both TIN and grid DEMs. Terra-Stream is flexible and allows users to choose from various models and parameters, yet our pipeline is designed to reduce (or eliminate) the need for manual intervention between stages. We have implemented TerraStream and present experimental results on real elevation point sets that show that our approach handles massive multi-gigabyte terrain data sets. For example, we can process a data set containing over 300 million points—over 20GB of raw data—in under 26 hours, where most of the time (76%) is spent in the initial CPU-intensive DEM construction stage. 1

The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We propose a framework for leveraging modern computing resources including multi-core distributed systems and general purpose GPU computing to reduce computational bottlenecks and accelerate DEM construction. We employ an I/O-efficient strategy using quad trees to automatically partition the lidar point clouds into a set of independent work bundles. We then distribute these work bundles to multiple GPU-equipped hosts which independently interpolate a portion of the DEM and return partial results. Finally, we gather the partial results and assemble the final DEM I/Oefficiently. Our approach balances I/O, computation, and network communication to reduce bottlenecks. Experimental results show that our approach scales linearly with the number of compute hosts, and achieves speed-ups of 25 × or greater using GPU computing. These results make it practical to use more complex interpolation methods such as regularized splines with tension, which provide geomorphological advantages over simpler interpolation methods such as linear interpolation, nearest neighbor interpolation, or natural neighbor interpolation.