OpenTopography v 3.2 Release Adds TIN-Based Gridding

Jun 16, 2011

We are pleased to announce the release of v. 3.2 of the OpenTopography system. This is a major update that includes several new enhancements to the lidar point cloud processing system to add functionality, speed up processing, and to improve the user experience. The most visible and important feature in this release is that OpenTopography now offers digital elevation model (DEM) generation via Martin Isenburg's (LASTools) high-performance streaming Delaunay triangulation. More information about features and updates in the v. 3.2 OpenTopography release are described below.

OpenTopography v. 3.2 Release Notes:
In addition to various bug patches and minor updates, the following are important updates as part of the v. 3.2 release.

Streaming TIN Algorithm for DEM Generation:
The TIN (Triangulated Irregular Network) interpolation function now offered by OpenTopography uses Martin Isenburg's high-performance code for generating raster digital elevation models (DEMs) from mass points via streaming Delaunay triangulation. A TIN model represents the landscape as a surface composed of contiguous and non-overlapping triangles constructed between the cloud of lidar returns. Within each triangle the surface is represented by a plane. A raster DEM is then constructed by sampling the triangulated surface elevation at the DEM nodes. More information on Isenburg's streaming TIN is available at: and in the following publication:

    Martin Isenburg, Yuanxin Liu, Jonathan Shewchuk, Jack Snoeyink, Tim Thirion, Generating Raster DEM from Mass Points via TIN Streaming, GIScience'06 Conference Proceedings, pages 186-198, September 2006.

The TIN algorithm nicely compliments OpenTopography's own Points2Grid local gridding algorithm that has been a mainstay of OT since the beginning. The TIN algorithm performs better in sparse or heterogeneous lidar sampling conditions and thus is highly effective when trying to construct bare earth terrain models in ares of low-ground return density (e.g., beneath dense vegetation).

Processing Speed Improvements:
OpenTopography is now architected to allow parallel running services and thus jobs complete more quickly. This parallelism in the services is displayed in the non-linear behavior of the job status display users see upon job submission.

Improved Point Count Estimates:
We've improved the way OpenTopography estimates the number of points that will be returned for a given user-defined bounding box selection. Although still an estimate, this more accurate count estimate should help users to better constrain the size of their jobs, thus meeting their OT processing limits, and resulting in fewer jobs rejected for exceeding processing limits.

Improved Linkage to Data Acknowledgment Language:
OpenTopography continues to strive to ensure that our data provider partners and their funders receive proper acknowledgement for the data we host. Dataset acknowledgment language is now available through a link on the lefthand side bar - "Data Acknowledgment" - and through a mouse-over link on every point cloud data page above the Google Map.

User Interface Updates:
- System Status box in sidebar under the data tab now shows live display of total number of jobs running in OpenTopography.
- Users can now deleted multiple jobs in their myLidarJobs list.

As always, we welcome your feedback and suggestions for new features and OpenTopography updates.