We are currently beginning to develop a machine learning system based on a Convolutional Neural Network (CNN) to automatize the description of micromorphological thin sections. This is carried out in collaboration with ULL Computer Science Professor Rafael Arnay. Our latest funding comes from a project of which he is PI: “Neural network Image tagging-based system for micromorphological description in archaeology”.
Although in recent years neural network-based image identification systems have become widespread, Their applications in microstratigraphy and micromorphology are meager. Micromorphological description is carried out manually, which requires a considerable time investment and entails a degree of subjective observation. Thus, our goal is to develop a tool to speed up and normalize the descriptive process.
The idea is to train a CNN model to create a micromorphology image database. We work with high resolution thin section scans that are processed using a sliding window technique. This allows us to isolate individual micromorphological features such as different particle, porosity and microstructure types. In a second stage, we will also create a web-based app for image tagging and encourage outside users to add images to the database, leading to increasing CNN autonomy.
Arnay, R., Fernández Aceituno, J., Mallol, C. 2020. Soil micromorphological image classification using deep learning: The porosity parameter. Applied Soft Computing. in press, Elsevier, 18/01/2021. https://doi.org/10.1016/j.asoc.2021.107093