Leveraging the power of multi-core platforms for large-scale geospatial data processing: Exemplified by generating DEM from massive LiDAR point clouds

  • Authors:

  • Xuefeng Guan;Huayi Wu

  • Affiliations:

  • The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China and The Joint Center for Intelligent Spatial Com ...

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2010

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Abstract

In recent years improvements in spatial data acquisition technologies, such as LiDAR, resulted in an explosive increase in the volume of spatial data, presenting unprecedented challenges for computation capacity. At the same time, the kernel of computing platforms the CPU, also evolved from a single-core to multi-core architecture. This radical change significantly affected existing data processing algorithms. Exemplified by the problem of generating DEM from massive air-borne LiDAR point clouds, this paper studies how to leverage the power of multi-core platforms for large-scale geospatial data processing and demonstrates how multi-core technologies can improve performance. Pipelining is adopted to exploit the thread level parallelism of multi-core platforms. First, raw point clouds are partitioned into overlapped blocks. Second, these discrete blocks are interpolated concurrently on parallel pipelines. On the interpolation run, intermediate results are sorted and finally merged into an integrated DEM. This parallelization demonstrates the great potential of multi-core platforms with high data throughput and low memory footprint. This approach achieves excellent performance speedup with greatly reduced processing time. For example, on a 2.0GHz Quad-Core Intel Xeon platform, the proposed parallel approach can process approximately one billion LiDAR points (16.4GB) in about 12min and produces a 27,500x30,500 raster DEM, using less than 800MB main memory.