Open AccessCHANGE DETECTION OF MOBILE LIDAR DATA USING CLOUD COMPUTING
Author(s) -
Kun Liu,
J. Boehm,
Christian Alis
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-309-2016
Subject(s) - spark (programming language) , computer science , point cloud , cloud computing , grid , context (archaeology) , voxel , big data , cache , artificial intelligence , photogrammetry , computation , representation (politics) , distributed computing , computer vision , parallel computing , data mining , algorithm , operating system , paleontology , geometry , mathematics , politics , political science , law , biology , programming language
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.