Saavedra, MiguelGutiérrez, GilbertoBernardo, Guillermo de2026-07-282026-07-282026Saavedra, M., Gutiérrez, G. & de Bernardo, G. k2-MS: A Compact Data Structure for Raster Datasets. Geoinformatica 30, 24 (2026). https://doi.org/10.1007/s10707-026-00578-y1573-7624https://hdl.handle.net/2183/48949This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10707-026-00578-y The data that support the findings of this study are available in WorldClim at https://www.worldclim.org/data/worldclim21.html. These data were derived from the following resource available in the public domain: WorldClim - Global Climate Data (Version 2.1).[Abstract]: In this work, we present the compact data structure k2-MS for the representation of raster coverages. k2-MS is based on a sequence of binary matrices (each represented by a k2-tree), which correspond to the binary encoding of the thematic variable values of the raster. The properties of the k2-MS data structure allow it to benefit from the processor instructions PDEP and PEXT, significantly reducing the access time to the structure. Through a series of experiments on different datasets, we evaluated the performance of our structure by comparing it with the k2-raster, one of the most competitive structures reported in the literature. On average, when comparing the best configurations of both approaches, k2-MS is 48% faster for Window queries and 43% faster for Window Range queries, while requiring about 63% of the storage space used by the k2-raster.eng© 2026, The Author(s), under exclusive licence to Springer Science Business Media, LLC, part of Springer NatureCompact Data StructuresRaster DataGeographic Information SystemsSpatial Data StructuresSpatial Algorithmsjournal articleembargoed access10.1007/s10707-026-00578-y