An equal‐area triangulated partition method for parallel Xdraw viewshed analysis

Abstract

This paper presents an effective equal-area triangulated partition method (EATP) for parallel viewshed computation based on Xdraw algorithm that is a viewshed analysis algorithm with lower overhead. Our approach is to first partition the target domain into eight 45 • octants along a coordinate-axis direction and diagonal line direction around a given observer. Each octant remains mutually independent on data dependency based on the principle of Xdraw algorithm.Then, each octant is repartitioned into equal-area sub-regions with the shape of the triangle according to the requirements of system platform such as the number of computing nodes or processors and the size of memory of each node. Our method is beneficial to group these triangle sub-regions for determination of their dependent relationships during the computation of visibility and distribution of each triangle sub-region to a suitable node. The unified expression of the sub-regions is simple for implementation of parallel Xdraw viewshed algorithm. The experimental results are evaluated to demonstrate a distinct improvement in computation performance compared with other partition methods such as equal-angle and simple equal-area methods. KEYWORDS equal-area triangulated partition, parallel computing, terrain visibility analysis, Xdraw viewshed algorithm INTRODUCTIONTerrain visibility between two cells with an elevation value, respectively, denotes whether they are visible each other on a terrain, which is also called inter-visibility analysis. A viewshed in terrain is a particular region of the terrain that is visible to a specified observer on the terrain. The viewshed analysis denotes the process whether the part of the terrain is determined to be visible to a specified observer at a given location on the terrain, which is widely applied spatial analysis to determine the visible regions of the terrain. 1,2 There are many applications in GISs (geographic information systems) including the optimal placement of observers according to visibility requirements, line-of-sight (LOS) communication problems, and terrain path planning with certain visibility properties. 3 However, the viewshed computation for a large-scale terrain viewshed analysis will be very time-consuming because a mass of interpolation operations had to be done in many viewshed analysis algorithms such as R2, R3, Xdraw, 4 reference plane algorithms, and so on. It has been a significant challenge of reducing a large amount of numeric calculation in the existing viewshed analysis algorithms. 5 In particular, the current DEM (digital elevation model) permits the applications to process large terrain with high resolution. 6 Viewshed calculation is usually implemented by emanating a LOS ray from the viewpoint or observer to a target point given an elevation value, respectively. 4,7 Considering the conventional sequential computation approaches are unable to provide effective support, the viewshed computation for large-scale terrain had to entail a massive number of LOS-based calculations. Hence, in ...

Quantitative Evaluation of the View of the Landscape Using a Visibility Analysis Optimization Algorithm

“…If the CR < 1, the consistency test is considered acceptable; otherwise, the matrix elements must be changed and rescaled according to Table 1 until the CR satisfies the consistency condition. The calculation of the CR is shown in Equation (15), where the RI (random consistency index) needs to be looked up in Table 2.…”

“…Different contexts require tailored optimization strategies. The existing improvements have centralized around speed and precision, as exemplified by Dou et al [15], who combined parallel computing with visibility algorithms for geographic data processing, and Wu et al [6]. who proposed the PDERL (Proximity-Direction-Elevation Reference Line) algorithm by establishing visibility algorithms in partial differential equation spaces, achieving precision close to R3 and efficiency comparable to XDraw.…”

PDERL: an accurate and fast algorithm with a novel perspective on solving the old viewshed analysis problem

“…On the basis of these traditional foundation methods and other similar studies, some scholars went on to improve the algorithm speed by reducing the computation on a single LOS (Liu et al 2011;Ying 2005), optimizing data exchange (Fishman et al 2009;Haverkort et al 2009), adopting cluster computing (Llobera et al 2010;Song et al 2016), or using graphics processing unit (GPU) hardware (Cauchi-Saunders and Lewis 2015;Zhao et al 2013). Research on improving viewshed analysis algorithm speed remains an important area of study Dou et al 2019;Li 2018).…”

“…While the XDraw and reference plane algorithms are still the two most popular algorithms for the current parallel research (e.g. Cauchi-Saunders and Lewis 2015;Dou et al 2019;Li 2018;Song et al 2016), their calculation errors have the greatest impact on viewshed algorithms, and most of the improvements still focus on speed while ignoring accuracy. XDraw is the least accurate algorithm among the traditional approximation methods (Kaučič and Žalik 2002), and reference plane has the same accuracy problem because of their similar perspective relationship.…”