The general objective of the project was achieved on the basis of an extensive microscopic image database created by the researcher. In order to facilitate the marking of a sufficient amount of objects for training the neural network, large image stitches were needed, rather than individual microphotographs. Over 40 high-resolution image stitches were prepared, each consisting of individual images in the range of 100-s and a few 1000-s of images. A series of Python scripts were written that enable the following: (i) sort and rename microphotographs, regardless of settings and the the hardware used for acquisition (ii) stack and enhance a series of photographs and (iii) stitch and error-correct a series or multiple series of photographs. Furthermore, in order to enable the automatic detection of mineral grains, fission tracks and their surface marks (i.e. etch pits), a Python machine learning script was constructed, which provides the opportunity to use about a dozen of network architectures for automated learning. Several thousand objects were taught to the neural networks on a set of image stitches. In most grain mounts the trained network could detect over 90% of the objects of interest. Owing to our stitching technique, objects to be detected at different magnifications could be trained at different zoom levels. A further Python script was developed on the top of the neural network, which extracts the outlines of detected objects. This script does not only enable measuring lengths, but also other geometric features such as the orientation and the roundness of the detected polygons. Finally, as overlapping tracks were detected as one polygon in transmitted light images, these results were overlaid by the detected etch pits on reflected light images. Overall, owing to three separate neural networks, track contours can be counted within grain contours and track openings can be counted within the detected tracks, facilitating automated track detection.
The described results were presented by the researcher at the Meeting of the International Mineralogical Association (Lyon, France) and at the Conference of the International Association for Mathematical Geosciences (Nancy, France). He has also co-organized and actively participated in 10th Sedimentary Provenance Analysis short course at the Department of Sedimentology and Environmental Geology, Georg-August Universität Göttingen with one talk, one practical class and a laboratory tour.
Besides scientific presentatitons, the researcher has given three popular scientific talks to Hungarian minorities living in Romania and Slovakia, namely school classes and interested teachers. The main focus of these talks was geological time and they were organized in an interactive way – children had to form groups and solve task themselves with the assistance of the researcher related to the research project. A fourth talk was given to two school classes in Spain (age group 8-9) in English language. Over 30 questions were asked by young pupils, which extended the session to over two hours, and as such can be considered as a particular success. Furthermore, the researcher has given a presentation to the Society of Kurdish Enlightened Women, a group of ca. 15 women living in the Kurdish part of Iraq, dedicated to the role of geosciences in understanding the current global environmental challenges.
Besides popular scientific presentations, the researcher also participated in the communication campaign of the „Science is Wonderful” event organized by the European Commission and maintained a blog on his personal webpage and posted 7 posts in a popular scientific language.
