Computing visibility on terrains in external memory

Abstract

Given an arbitrary viewpoint v and a terrain, the visibility map or viewshed of v is the set of points in the terrain that are visible from v. In this article we consider the problem of computing the viewshed of a point on a very large grid terrain in external memory. We describe algorithms for this problem in the cache-aware and cache-oblivious models, together with an implementation and an experimental evaluation. Our algorithms are a novel application of the distribution sweeping technique and use O(sort(n)) I/Os, where sort(n) is the complexity of sorting n items of data in the I/O-model. The experimental results demonstrate that our algorithm scales up and performs significantly better than the traditional internal-memory plane sweep algorithm and can compute visibility for terrains of 1.1 billion points in less than 4 hours on a low-cost machine compared to more than 32 hours with the internal-memory algorithm.

Efficient viewshed computation on terrain in external memory

“…Haverkort et al extended that into an algorithm to compute the viewshed on terrain stored in external memory, where the cells are sorted based on when they will be processed by the radial sweep. Table 3 compares EMVS to the results reported for IO_VS in [22], with the observer 1 m above the terrain. The EMVS values were averaged from the three corresponding values for Regions 1, 2 and 3 listed in Tables 1 and 2.…”

“…EMVS is more than 6 times faster than the algorithm of Haverkort et al [22] and also, it can process very large datasets; we used it on a 6.1 GB terrain. Finally, EMVS algorithm is quite simple to understand and to implement.…”

“…Recently, Haverkort et al [22] adapted van Kreveld's method to compute viewsheds on terrain stored in external memory; its I/O complexity is θ(sort(n)), where n is the number of points on the terrain and θ(sort(n)) is the minimum number of I/O operations required to sort n contiguous items stored in external memory.…”

“…The large number of disk accesses is optimized using the STXXL library [13]. Our algorithm is more than six times faster and much easier to implement than the excellent algorithm proposed by Haverkort et al [22]. This paper is organized as follows.…”

Improving Post-Earthquake Insurance Claim Management: A Novel Approach to Prioritize Geospatial Data Collection

“…The purpose of the analysis was to assess the expected coverage in terms of buildings and policies, and to highlight possible issues connected with the use of the proposed mobile mapping system. A cumulative viewshed analysis [27] along the path followed by the mobile mapping system has been carried out to select all buildings that are potentially visible from the acquired image locations (Figure 11). The algorithm iteratively loops through a set of input points and calculates viewshed maps, which depict the geographical area that is visible from the input point given digital elevation information.…”