Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 309-313, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-309-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 309-313, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-309-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

CHANGE DETECTION OF MOBILE LIDAR DATA USING CLOUD COMPUTING

Kun Liu, Jan Boehm, and Christian Alis Kun Liu et al.
  • Dept of Civil, Environ & Geomatic Eng, University College London, UK

Keywords: Point Cloud, Cloud Computing, Change Detection, Mobile Mapping, LiDAR

Abstract. Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.