The Segment Anything Model (SAM) introduces advanced transformer-based capabilities for geological image segmentation. While traditional geoscience applications rely on machine learning models like random forests and support vector machines, SAM’s attention mechanisms enable it to adapt to image data. This contribution evaluates SAM’s performance in segmenting rock outcrop images into three geological classes, using ground truth masks as references. Segmentation accuracy was assessed via intersection over union (IoU) scores across prompt types, including points and bounding boxes. A combination of bounding box and mask prompts provided the best results, particularly for large, distinct textures. Initial findings indicate SAM’s potential in geological segmentation, though further prompt refinement and expanded datasets are needed to address rock heterogeneity. Future work will focus on fine-tuning SAM for complex textures and integrating Laserscan-derived data for quantitative validation. This contribution underscores SAM’s promise in advancing automated geological segmentation applications.