A HADOOP-BASED ALGORITHM OF GENERATING DEM GRID FROM POINT CLOUD DATA

• Main Authors: X. Jian, X. Xiao, H. Chengfang, Z. Zhizhong, W. Zhaohui, Z. Dengzhong
• Format: Article
• Language: English
• Published: Copernicus Publications 2015-04-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1209/2015/isprsarchives-XL-7-W3-1209-2015.pdf

Airborne LiDAR technology has proven to be the most powerful tools to obtain high-density, high-accuracy and significantly detailed surface information of terrain and surface objects within a short time, and from which the Digital Elevation Model of high quality can be extracted. Point cloud data generated from the pre-processed data should be classified by segmentation algorithms, so as to differ the terrain points from disorganized points, then followed by a procedure of interpolating the selected points to turn points into DEM data. The whole procedure takes a long time and huge computing resource due to high-density, that is concentrated on by a number of researches. Hadoop is a distributed system infrastructure developed by the Apache Foundation, which contains a highly fault-tolerant distributed file system (HDFS) with high transmission rate and a parallel programming model (Map/Reduce). Such a framework is appropriate for DEM generation algorithms to improve efficiency. Point cloud data of Dongting Lake acquired by Riegl LMS-Q680i laser scanner was utilized as the original data to generate DEM by a Hadoop-based algorithms implemented in Linux, then followed by another traditional procedure programmed by C++ as the comparative experiment. Then the algorithm’s efficiency, coding complexity, and performance-cost ratio were discussed for the comparison. The results demonstrate that the algorithm's speed depends on size of point set and density of DEM grid, and the non-Hadoop implementation can achieve a high performance when memory is big enough, but the multiple Hadoop implementation can achieve a higher performance-cost ratio, while point set is of vast quantities on the other hand.