LiDAR technology provides the geospatial community with massive amounts of data for use in a variety of applications. As platforms continue to collect, process and store a seemingly unending supply of information, the key challenge is maintaining and delivering the data efficiently.
Leveraging more than a decade of LiDAR experience, PAR Government has been analyzing LiDAR data structures to support development of an algorithm to reduce storage requirements for LAS files that yields an 80 percent reduction in storage requirements. Based on a light-sensitive data structure with built-in compression and decompression the company's LiDAR Framework SDK is intended to seed LiDAR point clouds.
In addition to compressing data, a database indexing structure has been implemented to support query and retrieval of a variety of LiDAR data subsets based on geographic locations or user-desired positional (xyz) parameters, as well as temporal delimiters. The database feature also has been extended to include query syntax specific to user attributes of interest.
A compressed database storage structure lends itself well as an aggregator of all a user’s LiDAR collections into a single repository. Testing has revealed this as an important factor for compression performance since compression tends to increase with point density.
The LiDAR Framework provides an application interface to perform queries to identify the clouds of interest and then export those points with selected attributes and a specified quantization, yielding a desired compression. The database structure includes standard boolean delimiters. The database feature set can be extended to include query syntax specific to user attributes of interest.