Chaulio R. Ferreira, Marcus V. A. Andrade, Salles V. G. de Magalhães, and W. Randolph Franklin.
An efficient external memory algorithm for terrain viewshed computation.
ACM Trans. on Spatial Algorithms and Systems, 2016.
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This paper presents TiledVS, a fast external algorithm and implementation for computing viewsheds. TiledVS is intended for terrains that are too large for internal memory, even over 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 LRU 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 the 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 104000 x 104000 terrain on a modest computer with only 512MB of RAM took only 1 2 1 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 4-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.