首页|Studies from University of Cologne Provide New Data on Machine Learning (MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach)
Studies from University of Cologne Provide New Data on Machine Learning (MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach)
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NETL
NSTL
Copernicus Gesellschaft Mbh
Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Cologne, Germany, by NewsRx editors, research stated, “Micromorphological analysis using a petrographic microscope is one of the conventional methods to characterise microfacies in rocks (sediments) and soils. This analysis of the composition and structure observed in thin sections (TSs) yields seminal, but primarily qualitative, insights into their formation.” Financial supporters for this research include Deutsche Forschungsgemeinschaft. The news reporters obtained a quote from the research from University of Cologne: “In this context, the following question arises: how can micromorphological features be measured, classified, and particularly quantified to enable comparisons beyond the micro scale? With the Micromorphological Geographic Information System (MiGIS), we have developed a Python-based toolbox for the open-source software QGIS 3, which offers a straightforward solution to digitally analyse micromorphological features in TSs. By using a flatbed scanner and (polarisation) film, high-resolution red-green-blue (RGB) images can be captured in transmitted light (TL), cross-polarised light (XPL), and reflected light (RL) mode. Merging these images in a multi-RGB raster, feature-specific image information (e.g. light refraction properties of minerals) can be combined in one data set. This provides the basis for image classification with MiGIS. The MiGIS classification module uses the random forest algorithm and facilitates a semi-supervised (based on training areas) classification of the feature-specific colour values (multi-RGB signatures). The resulting classification map shows the spatial distribution of thin section features and enables the quantification of groundmass, pore space, minerals, or pedofeatures, such nodules being dominated by iron oxide and clay coatings. We demonstrate the advantages and limitations of the method using TSs from a loess-palaeosol sequence in Rheindahlen (Germany), which was previously studied using conventional micromorphological techniques. Given the high colour variance within the feature classes, MiGIS appears well-suited for these samples, enabling the generation of accurate TS feature maps. Nevertheless, the classification accuracy can vary due to the TS quality and the academic training level, in micromorphology and in terms of the classification process, when creating the training data.”
University of CologneCologneGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Copernicus Gesellschaft Mbh
