Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 577-582, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/577/2015/
doi:10.5194/isprsarchives-XL-3-W3-577-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
20 Aug 2015
NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS
J. Boehm and K. Liu Department of Civil, Environmental & Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK
Keywords: LiDAR, point cloud, database, spatial query, NoSQL, cloud storage Abstract. Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file) in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.
Conference paper (PDF, 1195 KB)


Citation: Boehm, J. and Liu, K.: NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 577-582, doi:10.5194/isprsarchives-XL-3-W3-577-2015, 2015.

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