Articles | Volume 73, issue 1
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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Cited articles

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Arnay, R., Hernández-Aceituno, J., and Mallol, C.: Soil micromorphological image classification using deep learning: The porosity parameter, Appl. Soft Comput., 102, 107093,, 2021. a
Arpin, T. L., Mallol, C., and Goldberg, P.: Short contribution: A new method of analyzing and documenting micromorphological thin sections using flatbed scanners: Applications in geoarchaeological studies, Geoarchaeology, 17, 305–313,, 2002. a, b
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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.