A Multi-Level Distributed Computing Approach to XDraw Viewshed Analysis Using Apache Spark

Abstract

Viewshed analysis is a terrain visibility computation method based on the digital elevation model (DEM). With the rapid growth of remote sensing and data collection technologies, the volume of large-scale raster DEM data has reached a great size (ZB). However, the data storage and GIS analysis based on such large-scale digital data volume become extra difficult. The usually distributed approaches based on Apache Hadoop and Spark can efficiently handle the viewshed analysis computation of large-scale DEM data, but there are still bottleneck and precision problems. In this article, we present a multi-level distributed XDraw (ML-XDraw) algorithm with Apache Spark to handle the viewshed analysis of large DEM data. The ML-XDraw algorithm mainly consists of 3 parts: (1) designing the XDraw algorithm into a multi-level distributed computing process, (2) introducing a multi-level data decomposition strategy to solve the calculating bottleneck problem of the cluster’s executor, and (3) proposing a boundary approximate calculation strategy to solve the precision loss problem in calculation near the boundary. Experiments show that the ML-XDraw algorithm adequately addresses the above problems and achieves better speed-up and accuracy as the volume of raster DEM data increases drastically.