Abstract. Two centuries ago William Smith produced the first geological map of England and Wales, an achievement that underlined the importance of mapping geological contacts and structures as perhaps the most fundamental skill set in earth science. The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost-functions to rapidly extract and measure structural features from point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3D) and two-dimensional (2D) datasets including high-resolution aerial imagery, virtual outcrop models, digital elevation models (DEMs) and geophysical grids. We demonstrate the algorithm with four diverse applications, where we extract: (1) joint and contact patterns in high-resolution orthophotographs; (2) fracture patterns in a dense 3D point cloud; (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (LiDAR) data, and; (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the objectivity and consistency of the interpretation process while retaining expert-guidance, and achieves significant improvements (35–65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.
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