Structure from motion (SfM) computer vision is a remote sensing method that is gaining popularity due to its simplicity and ability to accurately characterize site geometry in three dimensions (3D). While many researchers have demonstrated the potential for SfM to be used with unmanned aerial vehicles (UAVs) to model in 3D various geologic features, such as landslides, little is understood concerning how the selection of the UAV platform can affect the resolution and accuracy of the model. This study evaluates the resolution and accuracy of 3D point cloud models of a large landslide that occurred in 2013 near Page, Arizona, that were developed from various small UAV platform and camera configurations. Terrestrial laser scans were performed at the landslide and were used to establish a comparative baseline model. Results from the study indicate that point cloud resolution improved by more than 16% when using multi-rotor UAVs instead of fixed-wing UAVs. However, accuracy of the points in the point cloud model appear to be independent of the UAV platform, but depend principally on the selected camera and the image resolution. Additional practical guidance on flying various UAV platforms in challenging field conditions is provided for geologists and engineers.

Abstract:The effect of support material pretreatment temperature, prior to adding the active phase and promoters, on Fischer-Tropsch activity and selectivity was explored. Four iron catalysts were prepared on silica-stabilized alumina (AlSi) supports pretreated at 700 • C, 900 • C, 1100 • C or 1200 • C. Addition of 5% silica to alumina made the AlSi material hydrothermally stable, which enabled the unusually high support pretreatment temperatures (>900 • C) to be studied. High-temperature dehydroxylation of the AlSi before impregnation greatly reduces FeO·Al 2 O 3 surface spinel formation by removing most of the support-surface hydroxyl groups leading to more effectively carbided catalyst. The activity increases more than four-fold for the support calcined at elevated temperatures (1100-1200 • C) compared with traditional support calcination temperatures of <900 • C. This unique pretreatment also facilitates the formation of ε -Fe 2.2 C rather than χ-Fe 2.5 C on the AlSi support, which shows an excellent correlation with catalyst productivity.

This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire dam at high-priority level. This results in approximately 75% reduced flight time and model processing load, while still maintaining high model accuracy where needed. Models display stepped improvement in visual clarity and SfM reconstruction integrity by priority level, with the higher priority regions more accurately modeling smaller and finer features. A resolution map of the final tiered model is included. While this study focuses on multi-scale view planning for optical sensors, the methods potentially extend to other remote sensors, such as aerial LiDAR.

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Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.