An efficient nd-point data structure for querying flood risk

H. Liu, P. Van Oosterom, B. Mao, M. Meijers, R. Thompson

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
10 Downloads (Pure)

Abstract

Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ĝ€ static' maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

Original languageEnglish
Pages (from-to)367-374
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume43
Issue numberB4-2021
DOIs
Publication statusPublished - 2021
Event24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV - Nice, France
Duration: 5 Jul 20219 Jul 2021

Keywords

  • Database
  • Flood mapping
  • Hydrology
  • nD point clouds
  • Space filling curve

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