Scalability and Performance of LiDAR Point Cloud Data Management Systems: A State-of-the-Art Review

Chamin Nalinda Lokugam Hewage, Debra F. Laefer, Anh Vu Vo, Nhien An Le-Khac, Michela Bertolotto

Research output: Contribution to journalReview articlepeer-review


Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and novel data models (such as wide-column models) are considered. New structured query language (NewSQL) models are considered. The impacts of parallel architectures and data models are discussed with respect to theoretical perspectives and in the context of existing PCDM implementations. Based on the review, a methodical approach for the selection of parallel architectures and data models for highly scalable and performance-efficient PCDM system development is proposed. Finally, notable research gaps in the PCDM literature are presented as possible directions for future research.

Original languageEnglish (US)
Article number5277
JournalRemote Sensing
Issue number20
StatePublished - Oct 2022


  • data models
  • parallel architectures
  • performance
  • point cloud data
  • point cloud data management
  • scalability

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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