Comparative evaluation of deep learning architectures for bioclast classification in atomic force microscopy images

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.