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TiledViewshed - Efficient external memory viewshed computation
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TiledVS ====== Calculate an approximation to the viewshed of a specified observer in a rectangular elevation matrix. This algorithm has been specially designed to minimize I/O operations. Compilation ----------- To compile, use: g++ -O3 -o TiledVS tiledVS.cpp lz4.c Running ------- To run it, you first need to create a "tiles" folder in the current directory. Then: ./TiledVS NROWS NCOLS OBSERVER[0] OBSERVER[1] OBSERVER_HEIGHT RADIUS INPUT_FILE.hgt MEM [BLOCKSIZE_ROWS, BLOCKSIZE_COLS] > OUTPUT_FILE.vs where: * NROWS, NCOLS: Number of rows and columns in the input terrain file; * OBSERVER[0] and [1]: observer coordinates, in number of cells; * OBSERVER_HEIGHT: observer elevation above the terrain, in the same unit as the terrain file; * RADIUS: Observer's radius of interest, in number of cells; * INPUT_FILE.hgt: Input terrain file. Depending on whether you use 2 or 4 bytes per elevation value, it may be necessary to change the "typedef elev_t" (lines 47 and 48 on tiledVS.cpp) and recompile the code; * MEM: Maximum RAM memory size the algorithm should use, in Megabytes; * [BLOCKSIZE_ROWS, BLOCKSIZE_COLS]: Dimensions of each TiledMatrix block. This is optional. If not set, this will by automatically determined; * OUTPUT_FILE.vs: Output viewshed file, a ROWS x COLS bit matrix where 1 indicates a visible a cell and 0 a non-visible one. Bibliography ------------ This program is described in the publications: 1. Ferreira, C.R., et al., 2012. More efficient terrain viewshed computation on massive datasets using external memory. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’12, Redondo Beach, California, USA: ACM, 494–497. 2. Ferreira, C.R., et al. 2016, An efficient external memory algorithm for terrain viewshed computation. In: ACM Transactions on Spatial Algorithms and Systems 2.2 (2016): 6.
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