11-051.qxd 8/17/12 6:11 PM Page 927 Evaluating the Benefits of Octree-based Indexing for Lidar Data Abu Saleh Mohammad Mosa, Bianca Schön, Michela Bertolotto, and Debra F. Laefer Abstract early stages. Notably, these efforts for efficient storage and Very large three-dimensional (3D) point datasets are manipulation are independent of the specific data formats increasingly common, such as from Light Detection and (e.g., LAS) that are being developed by both profit and not- Ranging (lidar). Increasingly, there are attempts to exploit for-profit entities (e.g., ASPRS) to facilitate the exchange of these 3D point data sets beyond mere visualization. lidar data while preserving the properties of the raw data. However, current Spatial Information Systems provide only In this paper, the integration of all required functionality limited 3D support. Even commercial systems advertising for storing, indexing, manipulating, and analyzing 3D point in-built, 3D data types provide only minimal functionality. clouds within an Spatial Database Management System Specifically, there is no effective means of indexing large 3D (SDBMS) as a viable solution is considered with respect to an point datasets, which is crucial for efficient analysis and octree index implementation atop the Oracle Extensible engineering use. Also, many datasets are information rich Indexing Framework (OEIF) (Laefer et al., 2009). This (e.g., contain color or some other associated semantic approach greatly benefits spatial queries on a variety of 3D information), which has yet to be fully exploited. This paper point clouds. The particular contribution of the method presents the implementation in a commercial spatial described within this paper is its applicability to 3D point database of a spatial indexing technique using an octree clouds of varied distributions, as well as those that contains data structure and highlights its advantages for sparse, as additional semantic information. well as uniformly distributed, aerial lidar data. The imple- mentation outperforms an existing r-tree index within the software, and offers additional functionality of attribute- Background based 3D grouping. A major difficulty with large lidar datasets lies in their efficient storing and indexing within conventional Spatial Information Systems (SISs). Two main efforts for storing and Introduction analysis can be identified thus far. On one side, conven- In recent years there has been an ever increasing availability tional Geographic Information Systems (GISs) store the data of three-dimensional (3D) point cloud datasets, such as those spread across several files. This approach has been followed generated from Light Detection and Ranging (lidar), also since the 1960s, when GISs were used to deal with positional known as laser scanning (e.g., Beaulieu and Clavet, 2009; data or data with spatial extent (Shekhar and Chawla, 2003). Huang et al., 2009; Kukko and Hyyppä, 2009). Lidar is a Presently, different GIS vendors utilize their own proprietary remote sensing technology that has gained widespread file formats for the representation of such data. Yet the popularity due to its use in environmental and disaster storage and management of any vast dataset in these file management scenarios (e.g., Straatsmas and Baptist, 2008; based systems continue to have the following disadvantages: Olsen, 2009; Laefer and Pradhan, 2006). The introduction of (a) data inconsistency, (b) data redundancy, (c) lack of GPS plus GLONASS and “fitting” software facilitating data multi-user concurrency, and (d) lack of data integrity. collection with increased accuracy and recent innovations in Analysis in such a scenario relies on frequent import and flight path design demonstrate new possibilities for large- export transactions of these files into various Computer scale, 3D data collection in urban environments. This Aided Design (CAD) packages or other proprietary software, increasing availability of very large lidar-based point cloud for example, Cyclone from Leica Geosystems. This process is datasets (typically containing hundreds of millions of time intensive and requires the availability of staff trained points) has challenged existing means of effective exploita- on multiple software packages. tion; support for management of these datasets is still in its Database Management Systems (DBMSs) on the other hand, provide a means for effective data handling of large data volumes, while facilitating the retrieval of information in vast datasets through Structured Query Language (SQL). Abu Saleh Mohammad Mosa is at the University of The related technology of a Spatial Database Management Missouri Informatics Institute, 241 Engineering Building System (SDBMS) relies on a DBMS. In such an arrangement, West, Columbia, MO 65211, and previously at University many vendors provide spatial extensions to their Object College Dublin. Relational Database Management System (ORDBMS). PostGIS Bianca Schön is at University College Dublin, 40 Knockmaree, Dublin, Ireland 20. Michela Bertolotto is at University College Dublin, School of Photogrammetric Engineering & Remote Sensing Computer Science and Informatics, Dublin, Ireland 4. Vol. 78, No. 9, September 2012, pp. 927–934. Debra F Laefer is at University College Dublin, School of 0099-1112/12/7809–927/$3.00/0 Civil, Environmental, and Structural Engineering, Dublin, © 2012 American Society for Photogrammetry Ireland 4 ([email protected]). and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September 2012 927 11-051.qxd 8/17/12 6:11 PM Page 928 (http://www.postgis.org/), for example, is an open source Indexing 3D Point Cloud Data system for the storage of spatial data and conforms to the Indexing provides faster and more intelligent query execu- OGC (www.opengeospatial.org) standard. However, it does tions. Typically, data are structured into a hierarchical tree. not provide any in-built support for vast 3D point cloud Queries then need only follow certain branches and may data. Oracle Spatial, a commercial system, has recently avoid others. In principle, spatial queries on 3D point clouds included support for these data types. Their usefulness and could be performed directly on the entire dataset without capabilities are further evaluated within this paper for the indexing. In that scenario, for a particular spatial query, the purpose of comparing the efficiencies and capabilities of two corresponding spatial function would analyze an entire indexing approaches. dataset and then retrieve only relevant spatial objects. Looking forward, a scenario where many individuals However, since spatial functions are comparatively expen- and organizations are contributing data and trying to access sive, it would be rather costly to analyze an entire dataset. the subsequent combined data is easy to envision. In recent Instead, an appropriate spatial index needs to be created. calls for proposals, both Ireland’s National Road Authority1 Spatial indexes help retrieve candidate geometries for the and America’s Association for State Highway and Trans- specified spatial query, and the corresponding spatial portation Organizations2 have sought research proposals for function is then applied to this filtered dataset, which is the integration of both terrestrial and aerial remote sensing consequently reduced. In order to find the area of interest data based on increasing interest in this area. Such an and retrieve the most relevant dataset, the suitability of the environment will further strain the existing strategies to indexing method is critical. store these data in a meaningful way. Furthermore, there An effective algorithm for spatial indexing depends on will be a greater desire to exploit the 3D functionality of the the type and dimension of the spatial objects involved. For data. A key component of that is to have access to the efficient querying of point clouds, indexing must take all original data points. This will greatly facilitate the integra- three dimensions into account. The data must also be tion of multiple datasets. As such, the traditional approach processed in a timely fashion to facilitate efficient execution to store 3D point cloud data across various files or deriving of spatial queries. Some spatial indexing methods are other formats such as Digital Elevation Models (DEMs) for discussed in the following section, with a particular focus analysis purposes is likely to become less attractive. As on 3D point cloud data. such, new approaches must be considered to fully enable the increasingly rich and 3D nature of the data, such as Different Spatial Indexing Approaches better support of the raw point cloud data within SDBMSs. A spatial index organizes the spatial data and the underly- Applying an SDBMS for lidar data hosting allows for ing space in order to perform efficient execution of spatial improved data integrity, multi-user access, web access, and queries, either in an object-based or a space-based fashion. the use of SQL for spatial queries. However, such a spatial Object-based spatial indexes organize a dataset based on the system must support the data types for storing the geometry spatial objects distribution, while the space-based spatial in 3D Euclidean space (such as point, line, surface, and indexes subdivide the dataset based on a subdivision of the volume) that are based on a 3D geometric data model underlying space. (i.e., vector and/or raster data with their underlying geome- One of the most popular and enduring object-based try and topology). The