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We use structure from motion–multiview stereo (SM) terrain models developed from ground-based images and images acquired from uncrewed aircraft (aka drones) as a base map for three-dimensional (3-D) mapping on the walls of a deep canyon in the Panamint Range of eastern California, USA. The ability to manipulate the 3-D model with views from arbitrary look directions and broad scale range revealed structures that were invisible to conventional two-dimensional (2-D) mapping because of both the scale of the structures and their exposure on vertical to near-vertical cliff faces. The analysis supports field evidence for four phases of ductile deformation, with only one of the younger phases documented on early geologic maps of the area. The oldest deformational event (D1) produced the main metamorphic fabric and pre-dates Late Cretaceous plutons. This deformation produced a 200–250-m-thick high-strain zone localized along marbles at the top of the Kingston Peak Formation and lower Noonday Formation. Geometric analysis from the model suggests strongly that large sheath folds at scales of 100–300 m are developed within these marbles. Large measured finite strains indicate displacement across this apparent shear zone of at least 4–5 km and displacements of tens of kilometers are allowable, yet the structure is invisible to conventional mapping because the high-strain zone is stratabound. The main fabric shows two clear overprints and a third that is likely an even younger deformation. D2 and D3 generated tight to close, recumbent folds and open to tight, upright folds, respectively, both folding the main foliation with localized development of crenulation cleavages axial planar to the folds. An additional overprint shows no clear cross-cutting relationship with D2 or D3 fabrics and could be a manifestation of either of those events, although the deformation is spatially limited to a narrow shear zone beneath a brittle, dextral-normal fault with the same kinematics as a mylonitic fabric in a Cretaceous granite in the footwall. This observation suggests an extensional, core complex–style deformation to produce this structure. We suggest that 3-D mapping has the potential to revolutionize geologic mapping studies, particularly where steep topography provides 3-D views that are virtually invisible on conventional 2-D maps. Previously bewildering geologic puzzles can be solved by the ability to visualize large cliff exposures from arbitrary angles and map the features in true 3-D at resolutions to the centimeter level. Although this study emphasized intermediate scales imaged by a drone, our methods here are easily extended to larger scales using a crewed aircraft for imaging. We suggest these methods should be used routinely in frontier areas with steep terrain where aviation is already in use for access, but the methods can be employed anywhere steep terrain “hides” major rock exposures on conventional 2-D maps. 

We assess the accuracy of Structure-from-Motion/Multiview stereo (SM) terrain models acquired ad hoc or without high-resolution ground control to analyze their usage as a base for inexpensive 3D bedrock geologic mapping. Our focus is on techniques that can be utilized in field projects without the use of heavy and/or expensive equipment or the placement of ground control in logistically challenging sites (e.g., steep cliff faces or remote settings). We use a Terrestrial Light Detection and Ranging (LiDAR) survey as a basis for the comparison of two types of SM models: (1) models developed from images acquired in a chartered airplane flight with ground control referenced by natural objects located on Google Earth scenes; and (2) drone flights with a georeference established solely from camera positions located by conventional, differentially corrected Global Navigation Satellite systems (GNSS). We find that all our SM models are indistinguishable in scale from the LiDAR reference model. The SM models do, however, show rigid body translations and rotations, with translations generally within the 1–5 m size of the natural objects used for ground control, the resolution of the GNSS receivers, or both. The rigid body rotations can be attributed to a poor imaging plan, which can be avoided with survey planning. Analyses of point densities in various models show a limitation of Terrestrial LiDAR point clouds as a mapping base due to the rapid falloff of resolution with distance. In contrast, SM models are characterized by relatively uniform point densities controlled by camera optics, the numbers of images, and the distance from the target. This uniform density is the product of the Multiview stereo step in SM processing that fills areas between key points and is important for bedrock geologic mapping because it affords direct interpretation on a point cloud at a relatively uniform scale throughout a model. Our results indicate that these simple methods allow SM model construction to be accurate to the range of conventional GNSS with resolutions to the submeter, even cm, scale depending on data acquisition parameters. Thus, SM models can, and should, serve as a base for high-resolution geologic mapping, particularly in a steep terrain where conventional techniques fail. Our SM models appear to provide accurate visualizations of geologic features over km scales that allow detailed geologic mapping in 3D with a relative accuracy to the decimeter or centimeter level and absolute positioning in the 2–5 m precision of GNSS; a geometric precision that will allow unprecedented new studies of any geologic system where geometry is the fundamental data. 

Abstract Field geologists are increasingly using unmanned aerial vehicles (UAVs or drones), although their use involves significant cognitive challenges for which geologists are not well trained. On the basis of surveying the user community and documenting experts’ use in the field, we identified five major problems, most of which are aligned with well-documented limits on cognitive performance. First, the images being sent from the UAV portray the landscape from multiple different view directions. Second, even with a constant view direction, the ability to move the UAV or zoom the camera lens results in rapid changes in visual scale. Third, the images from the UAVs are displayed too quickly for users, even experts, to assimilate efficiently. Fourth, it is relatively easy to get lost when flying, particularly if the user is unfamiliar with the area or with UAV use. Fifth, physical limitations on flight time are a source of stress, which renders the operator less effective. Many of the strategies currently employed by field geologists, such as postprocessing and photogrammetry, can reduce these problems. We summarize the cognitive science basis for these issues and provide some new strategies that are designed to overcome these limitations and promote more effective UAV use in the field. The goal is to make UAV-based geological interpretations in the field possible by recognizing and reducing cognitive load.