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1. Geotechnical Characterization of Underground Mine Excavations from UAV-Captured Photogrammetric & Thermal Imagery

   Turner, R.M.; Bhagwat, N.P.; Galayda, L.J.; Knoll, C.S.; Russell, E.A.; MacLaughlin, M.M. (June 2018, Proceedings of the 52nd US Rock Mechanics / Geomechanics Symposium)

   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. 

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2. Generation of High-Resolution Orthomosaics from Historical Aerial Photographs Using Structure-from-Motion and Lidar Data

   https://doi.org/10.14358/PERS.22-00063R2  

   Suh, Ji Won; Ouimet, William (January 2023, Photogrammetric Engineering & Remote Sensing)

   This study presents a method to generate historical orthomosaics using Structure-from-Motion (SfM ) photogrammetry, historical aerial photographs, and lidar data, and then analyzes the horizontal accuracy and factors that can affect the quality of historical orthoimagery products made with these approaches. Two sets of historical aerial photographs (1934 and 1951) were analyzed, focused on the town of Woodstock in Connecticut, U.S.A. Ground control points (GCPs) for georeferencing were obtained by overlaying multiple data sets, including lidar elevation data and derivative hillshades, and recent orthoimagery. Root-Mean-Square Error values of check points (CPs ) for 1934 and 1951 orthomosaics without extreme outliers are 0.83 m and 1.37 m, respectively. Results indicate that orthomosaics can be used for standard mapping and geographic information systems (GIS ) work according to the ASPRS 1990 accuracy standard. In addition, results emphasize that three main factors can affect the horizontal accuracy of orthomosaics: (1) types of CPs, (2) the number of tied photos, and (3) terrain. 

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3. Coral Reef Monitoring by Scuba Divers Using Underwater Photogrammetry and Geodetic Surveying

   https://doi.org/10.3390/rs12183036  

   Nocerino, Erica; Menna, Fabio; Gruen, Armin; Troyer, Matthias; Capra, Alessandro; Castagnetti, Cristina; Rossi, Paolo; Brooks, Andrew J.; Schmitt, Russell J.; Holbrook, Sally J. (September 2020, Remote Sensing)

   null (Ed.)

   Underwater photogrammetry is increasingly being used by marine ecologists because of its ability to produce accurate, spatially detailed, non-destructive measurements of benthic communities, coupled with affordability and ease of use. However, independent quality control, rigorous imaging system set-up, optimal geometry design and a strict modeling of the imaging process are essential to achieving a high degree of measurable accuracy and resolution. If a proper photogrammetric approach that enables the formal description of the propagation of measurement error and modeling uncertainties is not undertaken, statements regarding the statistical significance of the results are limited. In this paper, we tackle these critical topics, based on the experience gained in the Moorea Island Digital Ecosystem Avatar (IDEA) project, where we have developed a rigorous underwater photogrammetric pipeline for coral reef monitoring and change detection. Here, we discuss the need for a permanent, underwater geodetic network, which serves to define a temporally stable reference datum and a check for the time series of photogrammetrically derived three-dimensional (3D) models of the reef structure. We present a methodology to evaluate the suitability of several underwater camera systems for photogrammetric and multi-temporal monitoring purposes and stress the importance of camera network geometry to minimize the deformations of photogrammetrically derived 3D reef models. Finally, we incorporate the measurement and modeling uncertainties of the full photogrammetric process into a simple and flexible framework for detecting statistically significant changes among a time series of models. 

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4. Data Driven Machine Learning for Estimation of PFAS Partitioning on Various Surface Materials

   Harsh V. Patel, Hyoshin Park (June 2023, AEESP Research and Education Conference 2023)

   This study, data driven machine learning model was developed to estimate the partitioning of Per- and Poly-fluoroalkyl Substances (PFAS) compounds during aqueous adsorption on various adsorbent materials with a vision to potentially replace the time-consuming and labor-intensive adsorption experiments. Various regression models were trained and tested using previously published data. 290 data points and 170 data points for activated carbon and mineral adsorbents, respectively, were mined for training the models and 10 data points were used to test the trained models. Statistical parameters, such as Root-Mean-Square Error (RSME), R-Squared, Mean Average Error (MAE), Mean Squared Error (MSE), etc., were used to compare the regression models. It was found that rational quadratic GPR (R-squared = 0.9966) and fine regression tree (R-Squared = 0.9427) models had the highest estimation accuracy for carbon-based and mineral-based adsorbents, respectively. These models were then validated for prediction accuracy using 10 data points from previous studies as an outer test set. Rational quadratic GPR was able to achieve 99% prediction accuracy for carbon-based adsorbent, while fine tree regression model was able to achieve 94% prediction accuracy. Despite such high estimation accuracy, the data mining process revealed the data shortage and the need for more research on PFAS adsorption to present real-world models. This study, as one of the first, shed a light on the determination of key parameters in aquatic chemistry with data mining and machine learning approaches. 

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5. Programmatic 3D Printing of a Revolving Camera Track to Automatically Capture Dense Images for 3D Scanning of Objects

   https://doi.org/10.1007/978-3-319-73600-6_39  

   Lamb, Nikolas; Banerjee, Natasha Kholgade; Banerjee, Sean (January 2018, Multimedia Modeling (MMM) 2018)

   Low-cost 3D scanners and automatic photogrammetry software have brought digitization of objects into 3D models to the level of the consumer. However, the digitization techniques are either tedious, disruptive to the scanned object, or expensive. We create a novel 3D scanning system using consumer grade hardware that revolves a camera around the object of interest. Our approach does not disturb the object during capture and allows us to scan delicate objects that can deform under motion, such as potted plants. Our system consists of a Raspberry Pi camera and computer, stepper motor, 3D printed camera track, and control software. Our 3D scanner allows the user to gather image sets for 3D model reconstruction using photogrammetry software with minimal effort. We scale 3D scanning to objects of varying sizes by designing our scanner using programmatic modeling, and allowing the user to change the physical dimensions of the scanner without redrawing each part. 

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