Conference Paper

Scalable algorithms for large high-resolution terrain data

DOI: 10.1145/1823854.1823878 Conference: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, COM.Geo 2010, Washington, DC, USA, June 21-23, 2010
Source: DBLP


In this paper we demonstrate that the technology required to perform typical GIS computations on very large high-resolution terrain models has matured enough to be ready for use by practitioners. We also demonstrate the impact that high-resolution data has on common problems. To our knowledge, some of the computations we present have never before been carried out by standard desktop computers on data sets of comparable size.

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    • "In contrast to our focus on algorithms which can be handled within internal memory, Mølhave et al. (2010) developed I/O-efficient external memory algorithms to cope with large-scale high resolution datasets. "
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    ABSTRACT: Hand in hand with the increasing availability of high-resolution digital elevation models (DEM), an efficient computation of land-surface parameters (LSPs) for large-scale digital elevation models becomes more and more important, in particular for web-based applications. Parallel processing using multi-threads on multi-core processors is a standard approach to decrease computing time for the calculation of local LSPs based on moving window operations (e.g. slope, curvature). LSPs which require non-localities for their calculation (e.g. hydrological connectivities of grid cells) make parallelization quite challenging due to data dependencies. On the example of the calculation of the LSP 'flow accumulation', we test the two parallelization strategies 'spatial decomposition' and 'two phase approach' for their suitability to manage non-localities. Three datasets of digital elevation models with high spatial resolutions are used in our evaluation. These models are representative types of landscape of Central Europe with highly diverse geomorphic characteristics: a high mountains area, a low mountain range, and a floodplain area in the lowlands. Both parallelization strategies are evaluated with regard to their usability on these diversely structured areas. Besides the correctness analysis of calculated relief parameters (i.e. catchment areas), priority is given to the analysis of speed-ups achieved through the deployed strategies. As presumed, local surface parameters allow an almost ideal speed-up. The situation is different for the calculation of non-local parameters which requires specific strategies depending on the type of landscape. Nevertheless, still a significant decrease of computation time has been achieved. While the speed-ups of the computation of the high mountain data set are higher by running the 'spatial decomposition approach' (3:2 by using four processors and 4:2 by using eight processors), the speed-ups of the 'two phase approach' have proved to be more efficient for the calculation of the low mountain and the floodplain data set (2:6 by using four processors and 2:9 by using eight processors). There, more non-localities in flat areas (e.g. filled sinks and floodplains) occur.
    Computers & Geosciences 08/2012; 44:1-9. DOI:10.1016/j.cageo.2012.02.023 · 2.05 Impact Factor
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