More efficient terrain viewshed computation on massive datasets using external memory

Ferreira, Chaulio R.; Magalhães, Salles V. G.; Andrade, Marcus V. A.; Franklin, W. Randolph; Pompermayer, André M.

doi:10.1145/2424321.2424398

Cited by 13 publications

(12 citation statements)

References 6 publications

(9 reference statements)

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“…This article extends our earlier algorithm, TILEDVS [Ferreira et al 2012], an efficient method to compute viewsheds on terrains in external memory. TILEDVS was an adaptation of RFVS, an internal memory algorithm proposed in Franklin and Ray [1994].…”

Section: Introductionmentioning

confidence: 91%

Untitled

2016

ACM Trans. Spatial Algorithms Syst.

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This article presents TILEDVS, a fast external algorithm and implementation for computing viewsheds. TILEDVS is intended for terrains that are too large for internal memory, even more than 100,000×100,000 points. It subdivides the terrain into tiles that are stored compressed on disk and then paged into memory with a custom cache data structure and least recently used algorithm. If there is sufficient available memory to store a whole row of tiles, which is easy, then this specialized data management is faster than relying on the operating system's virtual memory management. Applications of viewshed computation include siting radio transmitters, surveillance, and visual environmental impact measurement.TILEDVS runs a rotating line of sight from the observer to points on the region boundary. For each boundary point, it computes the visibility of all terrain points close to the line of sight. The running time is linear in the number of points. No terrain tile is read more than twice. TILEDVS is very fast, for instance, processing a 104,000×104,000 terrain on a modest computer with only 512MB of RAM took only 1 1 2 hours. On large datasets, TILEDVS was several times faster than competing algorithms, such as the ones included in GRASS. The source code of TILEDVS is freely available for nonprofit researchers to study, use, and extend.A preliminary version of this algorithm appeared in a four-page ACM SIGSPATIAL GIS 2012 conference paper, "More Efficient Terrain Viewshed Computation on Massive Datasets Using External Memory." This more detailed version adds the fast lossless compression stage that reduces the time by 30% to 40%, and many more experiments and comparisons.

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Section: Introductionmentioning

confidence: 91%

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2016

ACM Trans. Spatial Algorithms Syst.

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“…The proposed parallel algorithm, r.cuda.visibility, and the sequential version of the proposed algorithm, r.cpu.visibility, were compared with the reference algorithm. In addition, we conducted the same tests with the TiledVS viewshed implementation (Ferreira et al 2012).…”

Section: Algorithm Comparisonmentioning

confidence: 99%

“…An efficiency comparison was made between the r.cuda. visibility, r.cpu.visibility, and TiledVS (Ferreira et al 2012). The source code of our implementation is available at the following website: http://viewshed.s51mo.net.…”

Section: Introductionmentioning

confidence: 99%

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An IO-efficient parallel implementation of an R2 viewshed algorithm for large terrain maps on a CUDA GPU

Osterman

Benedičič

Ritosa

2014

International Journal of Geographical Information Science

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A rapid and flexible parallel approach for viewshed computation on large digital elevation models is presented. Our work is focused on the implementation of a derivate of the R2 viewshed algorithm. Emphasis has been placed on input/output (IO) efficiency that can be achieved by memory segmentation and coalesced memory access. An implementation of the parallel viewshed algorithm on the Compute Unified Device Architecture (CUDA), which exploits the high parallelism of the graphics processing unit, is presented. This version is referred to as r.cuda.visibility. The accuracy of our algorithm is compared to the r.los R3 algorithm (integrated into the open-source Geographic Resources Analysis Support System geographic information system environment) and other IO-efficient algorithms. Our results demonstrate that the proposed implementation of the R2 algorithm is faster and more IO efficient than previously presented IO-efficient algorithms, and that it achieves moderate calculation precision compared to the R3 algorithm. Thus, to the best of our knowledge, the algorithm presented here is the most efficient viewshed approach, in terms of computational speed, for large data sets.

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“…Examples include placement of telecommunication towers, placement of fire guard towers, survey of archaeological sites, military logistics, or survey of building sites. Thus, in recent years there has been a fair amount of work in the GIS literature dedicated to visibility analysis and the computations it entails (see the survey by Floriani and Magillo [11]), with many proposed algorithms [9,10,12,15,22,23] as well as publicly available implementations [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for the 1D Total Visibility-Index Problem

Berg

Afshani²,

Casanovaz³

et al. 2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

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Let T be a terrain, and let P be a set of points (locations) on its surface. An important problem in Geographic Information Science (GIS) is computing the visibility index of a point p on P , that is, the number of points in P that are visible from p. The total visibility-index problem asks for computing the visibility index of every point in P . Most applications of this problem involve 2-dimensional terrains represented by a grid of n × n square cells, where each cell is associated with an elevation value, and P consists of the center-points of these cells. Current approaches for computing the total visibilityindex on such a terrain take at least quadratic time with respect to the number of the terrain cells. While finding a subquadratic solution to this 2D total visibility-index problem is an open problem, surprisingly, no subquadratic solution has been proposed for the one-dimensional (1D) version of the problem; in the 1D problem, the terrain is an x-monotone polyline, and P is the set of the polyline vertices.We present an O(n log 2 n) algorithm that solves the 1D total visibility-index problem in the RAM model. Our algorithm is based on a geometric dualization technique, which reduces the problem into a set of instances of the red-blue line segment intersection counting problem. We also present a parallel version of this algorithm, which requires O(log 2 n) time and O(n log 2 n) work in the CREW PRAM model. We implement a naive O(n 2 ) approach and three variations of our algorithm: one employing an existing red-blue line segment intersection algorithm and two new approaches that perform the intersection counting by leveraging features specific to our problem. We present experimental results for both serial and parallel implementations on large synthetic and real-world datasets, using two distinct hardware platforms. Results show that all variants of our algorithm outperform the naive approach by several orders of magnitude on large datasets. Furthermore, we show that our new intersection counting implementations achieve more than 8 times speedup over the existing red-blue line segment intersection algorithm. Our parallel implementation is able to process a terrain of 2 24 vertices in under 1 minute using 16 cores, achieving more than 7 times speedup over serial execution.

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More efficient terrain viewshed computation on massive datasets using external memory

Cited by 13 publications

References 6 publications

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An IO-efficient parallel implementation of an R2 viewshed algorithm for large terrain maps on a CUDA GPU

An Efficient Algorithm for the 1D Total Visibility-Index Problem

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