1,1,3,1,4,5,6,10,10,1- 1Institute of Geological Sciences, University of Bern, Bern, Switzerland (david.mair@unibe.ch)
- 2Data Science Lab, University of Bern, Bern, Switzerland
- 3Institute of Geosciences, University of São Paulo, São Paulo, Brazil
- 4Institute of Physical Geography and Geoecology, RWTH-Aachen University, Aachen, Germany
- 5GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
- 6Fluvial Dynamics Research Group (RIUS), University of Lleida, Lleida, Spain
- 7School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington, New Zealand
- 8Amt der Vorarlberger Landesregierung, Bregenz, Austria
- 9Federal Office of Topography swisstopo, Wabern, Switzerland
- 10Unit of Hydraulic Engineering, University of Innsbruck, Innsbruck, Austria
- 11Natural Hazards and Risk Management, Geoconsult ZT GmbH, Puch bei Hallein, Austria
Obtaining information on the size and shape of individual sediment grains is fundamental to many geoscientific applications, as these properties provide insights into sediment transport and depositional processes. Conventional approaches for grain size and shape analysis rely on manual or semi-automated workflows and are therefore labor-intensive and time-consuming. Recent advances in deep learning, particularly in image segmentation and object detection, have enabled the development of automated methods for measuring grain size and shape. However, existing approaches typically are trained on homogeneous, task-specific datasets, which limits their ability to generalize across different data types and settings. Additionally, challenging image characteristics often compromise the segmentation accuracy.
We present an upgraded version 2.0 of the ImageGrains framework (Mair et al., 2024), which leverages a recently published next-generation segmentation model, Cellpose-SAM (Pachitariu et al., 2025). We re-trained this model on our newly released open-access dataset comprising 243 images and more than 29,000 manually annotated sediment grains. This dataset consists of images from various settings, including photographs of fluvial gravel, coarse pro-glacial deposits, and X-ray computed tomography scans of glacial till and marine sand. We use these data to benchmark the segmentation performance of the method against ground-truth annotations and to compare it to the performance of existing segmentation methods.
The results show that ImageGrains 2.0 achieves higher segmentation accuracy and improved generalization to previously unseen data compared to current state-of-the-art methods. When comparing the size and shape of grains predicted by the model with the ground truth annotations, we find that the increase in segmentation accuracy of our upgraded framework directly translates to more precise and realistic morphometric results, such as grain size distributions. We make our framework available to the community as a free and open-source installable Python package, as well as through interactive computing environments such as Jupyter Notebooks and a graphical user interface.
References
Mair et al. (2024): Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning, ESPL, 49, 1099–1116, https://doi.org/10.1002/esp.5755.
Pachitariu, et al. (2025): superhuman generalization for cellular segmentation, https://doi.org/10.1101/2025.04.28.651001.
How to cite: Mair, D., Witz, G., Do Prado, A., Garefalakis, P., Wild, A., Ville, F., Schuster, B., Horn, M., Österle, J., Fabbri, S., Litty, C., Achleitner, S., Leistner, S., Hiller, C., and Schlunegger, F.: Introducing ImageGrains 2.0 for improved grain size and shape measurements in 2D and 3D data from images of sediment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10505, https://doi.org/10.5194/egusphere-egu26-10505, 2026.
