A common theme we hear about LiDAR data from users is that the point data (or point cloud) is sitting on a shelf. Why is that?

The main reason is that the points themselves are not all that useful to most people. Yes, they can provide striking visualizations, especially if the RGB values from co-registered imagery are conflated to the points for photo-realistic renditions. But for most folks, it’s the data DERIVED from the point cloud that offers the most value for analysis and mapping. It’s the extraction of features, calculation of slope/volume/area, and the integration with geospatial analytical tools like proximity and line of sight that provide meaning to the data. Some derived data products are pretty straight forward to create with standard GIS software, and many tools are including increasing functionality for point clouds. Other data can be a bit more complicated and require specific tools and expertise. A good example is the generation of quality contour data for topographic maps. Automated software tools are fast and easy to use, but the results can often be disappointing, particularly for larger coverage areas such as a full municipality or county. Having a good process to reduce errors and manual cleanup can save lots of time and money in the long run and help create standard legacy data products that are useful to broad audiences and users.

GroundPoint staff has been processing LiDAR data into derived products for over 10 years. In this era of increasing commoditization of core geospatial data, the promise of advanced automated software solutions, and pressure to outsource labor overseas in order to reduce costs, GroundPoint has maintained a strong role in performing value added services that help get the job done right the first time. Fully automated processes can be too aggressive or not aggressive enough in how they filter errors and process data, and sending data overseas for post processing can add multiple iterative steps to achieve final quality control and acceptance of the data. While adding skilled labor to a project may add costs up front, it can mean huge savings in the long run for final project cost and ultimately for ensuring data quality.

Typical derived products from a raw LiDAR point cloud include reclassified point clouds (ground, water, trees), 3-D breaklines for terrain mapping, digital elevation models (bare earth), digital surface models (first return surface), contours, analysis of void areas (no signal), gridded slope and intensity data. We also perform formal Quality Control and Accuracy Assessments based on current FEMA and USGS standards.