Retrieval of pattern-based information from giga-cells categorical rasters — Concept and new software

Rapid development of computer technology together with the growing availability of giga-scale data sources brings new possibilities to geo-spatial analysis [1, 2]. We define giga-scale datasets as those having size exceeding 10⁹ cells, regardless of their physical scale. They may represent local regions at ultra-high resolution (of the order of centimeters) offered by LiDAR technology or global mosaics of satellite imagery or digital elevation models (DEMs) at medium resolution (of the order of 10-100 meters). Frequently, these giga-scale datasets are categorical rasters - products derived from processing of original data. Examples include land cover/land use (LCLU), landforms, vegetation, and urban maps. In such rasters important information is stored not only at the level of individual cells, but also, and maybe predominantly, at the level of patterns of the categories [3, 4]. Urban structures, plant habitats, geomorphological surfaces, and landscapes are examples of such patterns; they have collective functions and meaning and thus contain valuable information that cannot be inferred at the level of cell-based analysis.