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Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid.

Soo Hee Han1, Joon Heo, Hong Gyoo Sohn

  • 1School of Civil and Environmental Engineering, Yonsei University / 134 Sinchon-dong Seodaemun-gu, Seoul 120-749, Korea; E-Mails: scivile@yonsei.ac.kr ; sohn1@yonsei.ac.kr.

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PubMed
Summary
This summary is machine-generated.

This study introduces a parallel processing method for airborne laser scanning (ALS) data, significantly speeding up the creation of digital surface models (DSM) and digital terrain models (DTM). The parallel approach ensures identical results to sequential processing while offering superior performance with large datasets.

Keywords:
ALSDSMDTMLiDARPC clusterParallel processingVirtual grid

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Area of Science:

  • Geospatial Science
  • Computer Science
  • Remote Sensing

Background:

  • Airborne laser scanning (ALS) generates vast datasets requiring efficient processing.
  • Sequential processing methods can be time-consuming for large-scale ALS data.
  • Digital Surface Models (DSM) and Digital Terrain Models (DTM) are crucial geospatial products.

Purpose of the Study:

  • To develop and evaluate a parallel processing method for fast ALS data analysis.
  • To enable the efficient generation of DSM and DTM from large ALS point clouds.
  • To compare the performance and accuracy of parallel versus sequential processing.

Main Methods:

  • A parallel processing approach using a PC cluster and virtual grid.
  • Inverse Distance Weighting (IDW) interpolation for DSM creation.
  • Local minimum filtering for DTM generation from DSM.
  • Controlled handling of boundary data and interpolation centers across nodes.

Main Results:

  • Demonstrated significant speedup and efficiency gains with parallel processing.
  • Confirmed that parallel processing performance improves with increased computational overhead, processors, and data size.
  • Verified that the parallel algorithm operates in linear time.
  • Ensured identical DSM and DTM outputs compared to sequential processing.

Conclusions:

  • The proposed parallel processing method is effective for handling large ALS datasets.
  • This approach offers a scalable and efficient solution for generating geospatial models.
  • Parallel processing is a viable and accurate alternative to sequential methods for ALS data.