Toward a new approach for massive LiDAR data processing

V. H. Cao, K. X. Chu, N. A. Le-Khac, M. T. Kechadi, D. Laefer, L. Truong-Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS data acquisition, all return information of laser pulse is stored. Thus, ALS data are to be massive and complexity since the FWF of each laser pulse can be stored up to 256 samples and density of ALS data is also increasing significantly. Processing LiDAR data demands heavy operations and the traditional approaches require significant hardware and running time. On the other hand, researchers have recently proposed parallel approaches for analysing LiDAR data. These approaches are normally based on parallel architecture of target systems such as multi-core processors, GPU, etc. However, there is still missing efficient approaches/tools supporting the analysis of LiDAR data due to the lack of a deep study on both library tools and algorithms used in processing this data. In this paper, we present a comparative study of software libraries and new algorithms to optimise the processing of LiDAR data. We also propose new method to improve this process with experiments on large LiDAR data. Finally, we discuss on a parallel solution of our approach where we integrate parallel computing in processing LiDAR data.

Original languageEnglish (US)
Title of host publicationICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services
EditorsChongcheng Chen, Diansheng Guo, Yee Leung
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages135-140
Number of pages6
ISBN (Electronic)9781479977482
DOIs
StatePublished - Oct 13 2015
Event2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015 - Fuzhou, China
Duration: Jul 8 2015Jul 10 2015

Publication series

NameICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Other

Other2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015
CountryChina
CityFuzhou
Period7/8/157/10/15

Keywords

  • Kd-tree
  • LiDAR data
  • Parallel processing
  • TreeP

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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