Microscopic analysis of sediment micro fractions requires skilled scientists and is a very time consuming and expensive process. As micro particles are diagnostic of paleoenvironments, sedimentological processes and time ranges (biostratigraphy), image recognition through machine learning holds great potential for automating the identification of microfossils, mineral grains, anthropogenic remnants (micro plastics), and other micro particles. Therefore, automatic image recognition and sorting is likely to render data acquisition more cost- and time-effective, not only increasing traceability and reproducibility but also further reducing identification errors.
The purpose of this session is to gather experts from the geoscientific, engineering, and deep learning communities who are collaborating to apply machine and deep learning techniques to microscopic analysis. Given the novelty of this technique, we encourage contributions addressing development in this field ¬¬– for example, production of training sets, laboratory and camera/video setups/designs, applied robotics, and algorithmic developments. We also welcome any geological studies applying machine learning and numerical approaches (including biometric studies) via image recognition of microscopic images.