Open AccessCLASSIFICATION OF BIG POINT CLOUD DATA USING CLOUD COMPUTING
Author(s) -
K. Liu,
J. Boehm
Publication year - 2015
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-xl-3-w3-553-2015
Subject(s) - spark (programming language) , computer science , big data , geospatial analysis , cloud computing , point cloud , context (archaeology) , data science , point (geometry) , data processing , volume (thermodynamics) , data mining , artificial intelligence , database , operating system , geography , physics , geometry , mathematics , archaeology , quantum mechanics , programming language , cartography
Point cloud data plays an significant role in various geospatial applications as it conveys plentiful information which can be used fordifferent types of analysis. Semantic analysis, which is an important one of them, aims to label points as different categories. Inmachine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging dueto the growing data volume. In this paper, we address point data classification in a big data context. The popular cluster computingframework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark forlarge-scale point data processing