The identification and classification of bioclasts in limestone are fundamental tasks in petrographic analysis, traditionally performed through optical microscopy and expert-driven interpretation. While Atomic Force Microscopy (AFM) provides high-resolution topographic information at micro- to nanoscales, the analysis of AFM images for bioclast identification remains challenging due to complex surface morphologies, multi-scale textures, and the lack of direct correspondence with optical observations. Existing approaches rely heavily on manual inspection and complementary imaging techniques, resulting in time-consuming and non-scalable workflows. In this work, we address the problem of automated bioclast classification directly from AFM topographic images by systematically evaluating eight state-of-the-art deep learning architectures under multiple input resolutions and training strategies. In particular, we investigate the impact of image resolution, progressive resizing, and multi-scale feature modeling on classification performance. Our comparative analysis reveals a strong positive correlation between input resolution and performance, with progressive resizing consistently improving model robustness. Among the evaluated architectures, HRNet-based models demonstrate superior performance in capturing hierarchical geological textures, achieving a maximum F1-score of 79.1. Furthermore, an ensemble of the three best-performing HRNet variants further enhances classification accuracy, reaching an F1-score of 82.1.
Moya et al. (Mon,) studied this question.