Volume IV-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 121-124, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-121-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 121-124, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-121-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2017

19 Oct 2017

EFFICIENT LIDAR POINT CLOUD DATA MANAGING AND PROCESSING IN A HADOOP-BASED DISTRIBUTED FRAMEWORK

C. Wang1, F. Hu2, D. Sha2, and X. Han1 C. Wang et al.
  • 1Hainan Geomatics Center, National Administration of Surveying, Mapping and Geoinformation of China, HaiKou, HaiNan, 570203, China
  • 2Department of Geography and GeoInformation Science and Center for Intelligent Spatial Computing, George Mason University, Fairfax, VA, 22030-4444, USA

Keywords: Lidar Data, Point Cloud, Hadoop, PCL, HDFS, MapReduce, GIS, Distributed Computing

Abstract. Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping,city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop’s storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.