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RSS FeedsRemote Sensing, Vol. 15, Pages 761: A Multi-Level Distributed Computing Approach to XDraw Viewshed Analysis Using Apache Spark (Remote Sensing)

 
 

28 january 2023 12:32:36

 
Remote Sensing, Vol. 15, Pages 761: A Multi-Level Distributed Computing Approach to XDraw Viewshed Analysis Using Apache Spark (Remote Sensing)
 


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.


 
95 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 15, Pages 760: Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia (Remote Sensing)
Remote Sensing, Vol. 15, Pages 762: Reducing Model Error Effects in El Niño–Southern Oscillation Prediction Using Ensemble Coupled Data Assimilation (Remote Sensing)
 
 
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