A method for preserving three spatial features in the upscaling of categorical raster data

Raster resampling can be used to modify the resolution of raster data to satisfy specific application requirements for geographical information systems (GIS). However, with an increase in raster cell size, a process known as upscaling, various spatial features are inevitably lost, resulting in reduced data accuracy. Categorical raster data refer to a raster dataset where each specific raster value corresponds to a category, such as land use types or vegetation cover, rather than continuous numerical values. To improve the accuracy of upscaled data, this study proposes a method for preserving the shape, topological, and area features in categorical raster upscaling. First, we refined the shape index calculation to accurately assess the shape of the raster zones and corrected the shape errors using neighborhood operations. Second, we resolved the topological errors by reassigning the cells between the raster zones. Finally, we calculated the number of cells that needed adjustment in each zone and reassigned the cells on the zone boundaries accordingly, to reduce the overall area error. The results demonstrated a 30.5235 % improvement in the accuracy, compared with the accuracy of the nearest neighbor method for upscaling from 5 to 10 m. The effectiveness of the proposed method decreased with increasing target cell size, with the method being ineffective at 35 m. Furthermore, the method demonstrates wide applicability across different datasets. By efficiently and simultaneously maintaining these spatial features during upscaling, our method can offer users more accurate resampled datasets as input for GIS applications, thereby enhancing the precision of the outputs. • Simultaneous preservation of shape, topological, and area features during upscaling. • Improved shape index accurately assesses disconnected raster zones. • A novel neighborhood operation maintains the shape of elongated raster zones. • Around 30 % of accuracy improvement over the conventional method was achieved. • Stable performance across datasets of varying shape complexities was observed.