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Photogrammetric data collection and analysis techniques are increasingly being used for geotechnical characterization of rock masses, and rock slopes, in particular. There is a growing selection of software packages that can create georeferenced digital 3D models from a photoset and control points. Although each software package is able to create the desired point clouds, different techniques are used to produce them. For a geotechnical investigation, it is important to understand the accuracy of the software being used in order to have confidence in the reliability of the digital 3D models that are created. In a study similar to one conducted in conjunction with the GoldenRocks ARMA conference in 2006 (and described in Tonon and Kottenstette, 2006), a rock outcrop was selected to be the location for a digital photogrammetry model comparison. Two sets of control points were surveyed on the rock outcrop; one set was provided for the creation of each model, and one set was used to evaluate the accuracy of the model by measuring the difference in the location of the point in the model and in the survey data. An unmanned aerial vehicle (UAV) was used to collect video footage of the site. A set of still frames were extracted from the video that contain overlapping images of the rock outcrop. The set of image files was used to create models with the following photogrammetry software packages: Bentley ContextCapture, Agisoft PhotoScan, and Pix4Dmapper. The accuracy of each of the software packages was compared by quantifying the error in the control points and check points between the model and the field survey. As this comparison is intended to provide guidance for selecting software tools to aid in rock mass characterization, other features were evaluated as well, including user-friendliness. Understanding the accuracy of digital photogrammetry software is critical for justifying the use of such models in a geotechnical investigation. The advantages of these models are numerous but of little value if the data provided by the models do not adequately represent the field conditions. Bentley ContextCapture was found to have the least error in the control points and Pix4Dmapper was found to have the least error in the check points. The Bentley ContextCapture model also had the highest resolution, closely followed by the Pix4Dmapper model. Based on these qualities and several others including the general usability, Bentley ContextCapture creates the most effective models for potential geotechnical investigations. 

Geotechnical characterization of rock masses in underground mines often involves physical measurements in supported excavations. However, unsupported stopes and drifts prevent safe access for mapping by geotechnical personnel. The advent of inexpensive, open platform unmanned aerial vehicles (UAVs) allows geotechnical personnel to characterize hazardous rock masses by utilizing traditional photogrammetric and FLIR (forward looking infrared) imagery techniques. The photogrammetric imagery can be used to capture geological structural data from the rock mass for kinematic and numerical analyses, as well as for generating geological models. In particular, the FLIR imagery has the potential to assist in identifying areas of loose rock, which typically goes unnoticed until it becomes a hazard. This paper summarizes the results of a study involving UAV flights underground at the Barrick Golden Sunlight Mine, the generation of 3D models from UAV-captured imagery, and the identification of geological data from photogrammetry models. Results confirm that the combination of off-the-shelf technologies used in this study can be successfully employed as a geotechnical tool in the underground mining environment.