A new generation of Point Cloud Data Management Systems.

Currently large data sets, such as country wise LiDAR scans, are being collected and combined with large collections of semantically rich objects to form a new source of knowledge for urban planing and smart cities. Such data is often stored using domain specific file-based solutions. Although this allows efficient access to the data in its original format, data isolation, and application dependency on data formats are major drawbacks of this approach. Furthermore, complex ad-hoc queries are hard to express, particularly when faced with the challenge to combine numerous data sources. File-based solutions have also poor vertical and horizontal scalability. To integrate different data sets with spatial data, and to have efficient and flexible data exploration a Spatial Data Management System (SDBMS) is advised. However, current solutions are not capable of handling efficiently large LiDAR data sets due to the high cost of converting, loading, indexing and compressing Point Cloud data [4]. In this paper we present an efficient data management layer for geo-spatial data analysis with special emphasis on LiDAR data. It provides fast data ingestion through a specialized data loader, but also in-situ data access to LAS/LAZ data repositories containing billions of points. Through in-memory indexes, efficient data filtering and a refinement stage based on a divide and conquer approach our solution outperforms the well known PostgreSQL with Point Cloud extension. For complex queries, thanks to its efficient multi-core parallelism, our solution is an order magnitude faster than the file-based solution Rapidlasso LAStools