This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from 0.55 cm spatial resolution aerial photographs, acquired by unmanned aerial vehicles (UAV), over the Wiborg Rapakivi granite outcrops in the islands off the coast of the Loviisa Region in Southern Finland. The workflow comprised training a U-Net CNN using a small subset of photographs with manually traced fractures for optimizing the network parameters using the root mean squared propagation optimizer and sigmoidal focal loss function for semantic segmentation of input images and pixel-wise identifi­ cation of fracture traces. The ridge detection algorithm was then applied to the U-Net prediction results, followed by vectorization of the fracture-traces pixels as vector polylines representing the traces of fractures. Both in­ tensity values of the pixels and topological connectivity were used in the process of vectorization. Quantitatively the results were assessed using various accuracy assessment metrics. Qualitative evaluations of the results were implemented by comparisons of orientations and length-frequency distributions of automatically- and manuallymapped traces.