HPC-ICTM: The Interval Categorizer Tessellation-Based Model for High Performance Computing

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Abstract

This paper presents the Interval Categorizer Tessellation-based Model (ICTM) for the simultaneous categorization of geographic regions considering several characteristics (e.g., relief, vegetation, land use etc.). Interval techniques are used for the modelling of uncertain data and the control of discretization errors. HPC-ICTM is an implementation of the model for clusters. We analyze the performance of the HPC-ICTM and present results concerning its application to the relief/land-use categorization of the region surrounding the lagoon Lagoa Pequena (RS, Brazil), which is extremely important from an ecological point of view.