Initial version of SCALGO software for processing massive terrain data sets released

October 06, 2011 -- Scalable Algorithmics (SCALGO) was founded in 2009 with the mission to bring cutting-edge massive terrain data processing technology to market. The SCALGO technology is based on more than two decades of basic and applied research on I/O-efficient and geometric algorithms at Center for Massive Data Algorithmics (MADALGO) at Aarhus University in Denmark and at Duke University in the US, in collaboration with industry LIDAR and environmental GIS application experts. Software based on the technology can handle much larger terrain datasets on a normal desktop than most current software and thus it eliminates the need for accuracy-decreasing data thinning. The use of novel mathematical and algorithmic techniques also means that the software works provably efficient on all input datasets, delivering a completely specified output without the use of cumbersome work-flows such as those introduced by data tiling.

The SCALGO software portfolio includes software packages  SCALGO Model and  SCALGO Hydrology, which have successfully been used to process terrain datasets with more than 50 billion data elements on a normal desktop computer. SCALGO Model can be used to construct raster and TIN terrain models from massive terrain points (such as LIDAR), as well as to simplify massive raster terrain models in a fully-specified and user-controlled way. SCALGO Hydrology can be used to perform basic hydrological modeling on massive raster terrain models, including computing flow accumulation, watersheds, bluespots (maximal depressions) and the effect of rising sea-level.

More information about SCALGO technology, software products and services can be found at  scalgo.com. The website also contains more information about some of the projects where SCALGO technology and software has been used successfully ( scalgo.com/technology/success.php), including an  interactive visualization of the results of using the SCALGO hydrology software on a massive and almost global terrain model to access the risk of flooding.



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