The recent availability of detailed geographic data permits terrain applications to process large areas at high resolution. However the required massive data processing presents significant challenges, demanding algorithms optimized for both data movement and computation. One such application is viewshed computation, that is, to determine all the points visible from a given point p. In this paper, we present an efficient algorithm to compute viewsheds on terrain stored in external memory. In the usual case where the observer's radius of interest is smaller than the terrain size, the algorithm complexity is θ(scan(n 2 )) where n 2 is the number of points in an n × n DEM and scan(n 2 ) is the minimum number of I/O operations required to read n 2 contiguous items from external memory. This is much faster than existing published algorithms.
We present a better algorithm and implementation for external memory viewshed computation. It is about four times faster than the most recent and most efficient published methods. Ours is also much simpler. Since processing large datasets can take hours, this improvement is significant. To reduce the total number of I/O operations, our method is based on subdividing the terrain into blocks which are stored in a special data structure managed as a cache memory.The viewshed is that region of the terrain that is visible by a fixed observer, who may be on or above the terrain. Its applications range from visual nuisance abatement to radio transmitter siting and surveillance.
We describe a surface compression technique to lossily compress elevation datasets. Our approach first approximates the uncompressed terrain using an over-determined system of linear equations based on the Laplacian partial differential equation. Then the approximation is refined with respect to the uncompressed terrain using an error metric. These two steps work alternately until we find an approximation that is good enough. We then further compress the result to achieve a better overall compression ratio. We present experiments and measurements using different metrics and our method gives convincing results.
Abstract-We present an algorithm (and implementation) which sites multiple (perhaps hundreds) of observers on a DEM terrain that is too large to store in internal memory. Tests show it to use a median of fifteen percent fewer observers to obtain the same joint visibility index (coverage) on huge terrains, compared to a naive partitioning of the terrain into subregions. This will permit more efficient positioning of facilities such as mobile phone towers, fire observation towers, and vigilance systems.
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