GRASS GIS A Point Cloud (LiDAR) Evaluation Resource

Point clouds are flourishing today as a primary source of geospatial data content. The size or volume of these point cloud datasets, however, often constitute a real big data challenge for many users. As a result, the practical use, evaluation and visualization of point cloud data can be limited, difficult, and/or time consuming. Fortunately, GRASS GIS offers a powerful open source pathway to facilitate access and analysis on large stores of point cloud data. This presentation will concentrate on demonstrating some practical use cases of GRASS GIS for typical point cloud data integrity and quality control analyses. Typical tests that evaluate point cloud quality characteristics such as spatial density, coverage, regularity and vertical agreement will be covered. Practical demonstrations will be shared using a variety of point cloud data derived from both LiDAR and photogrammetry sources. The key to these open source processing capabilities lies primarily in a single GRASS function (r.in.lidar) that reads and simultaneously examines the point cloud data statistically in the raster domain. When paired with other GRASS and FOSS4G tools, a compelling case can be made for the use of open source software as an efficient basis for handling diverse point cloud data analysis and spatial data exploration tasks.