Comparative analysis of data structures for storing massive tins in a DBMS

Kavisha Kavisha, Hugo Ledoux, Jantien Stoter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m2. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.

Original languageEnglish
Title of host publicationInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherISPRS
Pages123-130
Volume41-B2
DOIs
Publication statusPublished - 2016
Event23rd International Society for Photogrammetry and Remote Sensing Congress - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016
Conference number: 23

Conference

Conference23rd International Society for Photogrammetry and Remote Sensing Congress
Abbreviated titleISPRS 2016
Country/TerritoryCzech Republic
CityPrague
Period12/07/1619/07/16

Keywords

  • Massive
  • point clouds
  • TINs
  • DBMS

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