Towards robust fracture mapping: benchmarking automatic fracture mapping in 2D outcrop imagery

Analysis of fracture geometries, orientation, distribution, and connectivity is commonly conducted to characterise the mechanical and hydraulic properties of a fractured rock mass and to interpret its deformation history. These studies increasingly leverage high-resolution drone imagery (e.g., orthomosaics or orthophotographs). However, consistently and accurately extracting fracture traces from these large datasets remains a persistent challenge. In this contribution, we present a harmonised benchmarking dataset, FraXet, for pixel-wise fracture segmentation of combined high-resolution RGB orthophotographs and digital elevation models (DEMs) (ground sampling distance ranging from approximately 0.5 to 32 mm). FraXet curates images from three publicly available datasets, totalling 8953 256 × 256 RGB + DEM patches spanning diverse lithologies and imaging conditions (ground sampling distance, illumination, etc.) to systematically assess the effectiveness of conventional image-processing approaches for fracture extraction (Canny, Sobel, Gabor, Sato, and phase congruency) and two deep-learning (DL) models (U-Net and SegFormer). Quantitative comparisons using image-quality (e.g., MSE, PSNR), segmentation (e.g., Precision, Recall, F1, IoU), and new task-specific error metrics show that the deep models substantially outperform classical filters (F1 ≈ 0.3–0.5 vs. ± 0.29) and produce smoother, more continuous fracture traces. Training on the combined dataset (M_all) improves cross-site generalisation compared to models trained on individual sub-datasets used in this study. Probability maps derived from the DL approaches enable confidence-based triage and visualisation of model uncertainty. This work establishes a unified benchmark, curated dataset, and reproducible baseline for developing robust automated fracture-detection tools.