Nested spatial data structures for optimal indexing of LiDAR data

In this paper we present a flexible framework for creating spatial data structures to manage LiDAR point clouds in the context of spatial big data. For this purpose, standard approaches typically include the use of a single data structure to index point clouds. Some of them use a hybrid two-tier solution to optimize specific application purposes such as storage or rendering. In this article we introduce a meta-structure that can have unlimited depth and a custom, user-defined combination of nested structures, such as grids, quadtrees, octrees, or kd-trees. With our approach, the out-of-core indexing of point clouds can be adapted to different types of datasets, taking into account the spatial distribution of the data. Therefore, the most suitable spatial indexing can be achieved for any type of dataset, from small TLS-based scenes to planetary-scale ALS-based scenes. This approach allows us to work with overlapping datasets of different resolutions from different acquisition technologies in the same structure.