Articles | Volume 73, issue 1
https://doi.org/10.5194/egqsj-73-69-2024
https://doi.org/10.5194/egqsj-73-69-2024
Research article
 | 
26 Jan 2024
Research article |  | 26 Jan 2024

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

Mirijam Zickel, Marie Gröbner, Astrid Röpke, and Martin Kehl

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Short summary
With our open-source toolbox, MiGIS for QGIS 3, we intend to advance digital micromorphological analysis. This approach focuses on the classification of micromorphological constituents based on their distinct colour values (multi-RGB signatures), acquired using flatbed scanning of thin sections in different modes (transmitted, cross-polarised, and reflected light). The resulting thin section maps enable feature quantification, visualisation of spatial patterns, and reproducibility.