Doctor of Philosophy, The Ohio State University, 2004, Geodetic Science and Surveying

At the present time, the algorithms involved in photogrammetric activities are changing substantially. Photogrammetric activities are expanding to accommodate features other than points, such as straight-line, in the photogrammetric solution. Along this line of expansion, in this study, conic sections (circles and ellipses) were formulated as computational entities for the solution of single-photo resection, camera calibration, and photogrammetric triangulation. Feature extraction is the entry point for the use of conics. In this research, an integrated and robust algorithm for the extraction of multiple conic was developed. This algorithm addresses the extraction of conics at three stages. The first two stages are based on parametric data extraction under the general framework of Hough Transform. A crucial contribution in this algorithm is the notion of the directional attribute, which facilitates the best use of the information provided by the parametric extraction. The third stage takes the form of a probabilistic inference rule. Successful performance was demonstrated on simulated and real data. Analytical representation of conic sections was required in this study. To this end, a least-squares conic fitting algorithm was developed. This algorithm minimizes the distance between the observed points and a modified conic along the normal to its tangent for every observed point. This algorithm neglects second order terms induced by the quadratic nature of the conic equation at each step and, therefore, requires some iterations. Experimental finding based on simulated and real data confirmed the good performance of this algorithm. Two non-linear conics-based algorithms for a single-photo resection were developed (CCA and PTFA). The CCA is very robust to the selection of the initial approximations. Slightly different from zero and exactly zero approximations are sufficient to initialize the camera position and the rotation angles, respectively. On the other hand, the (open full item for complete abstract)

Committee: Anton Schenk (Advisor) Subjects: Geodesy

Doctor of Philosophy, The Ohio State University, 2008, Geodetic Science and Surveying

One of the major tasks in digital photogrammetry is to determine the orientation parameters of aerial imageries correctly and quickly, which involves two primary steps of interior orientation and exterior orientation. Interior orientation defines a transformation to a 3D image coordinate system with respect to the camera's perspective center, while a pixel coordinate system is the reference system for a digital image, using the geometric relationship between the photo coordinate system and the instrument coordinate system. While the aerial photography provides the interior orientation parameters, the problem is reduced to determine the exterior orientation with respect to the object coordinate system. Exterior orientation establishes the position of the camera projection center in the ground coordinate system and three rotation angles of the camera axis to represent the transformation between the image and the object coordinate system. Exterior orientation parameters (EOPs) of the stereo model consisting of two aerial imageries can be obtained using relative and absolute orientation. EOPs of multiple overlapping aerial imageries can be computed using bundle block adjustment. Bundle block adjustment reduces the cost of field surveying in difficult areas and verifies the accuracy of field surveying during the process of bundle block adjustment. Bundle block adjustment is a fundamental task in many applications, such as surface reconstruction, orthophoto generation, image registration and object recognition. Point-based methods with experienced human operators are processed well in traditional photogrammetric activities but not the autonomous environment of digital photogrammetry. To develop more robust and accurate techniques, higher level objects of straight linear features accommodating elements other than points are adopted instead of points in aerial triangulation. Even though recent advanced algorithms provide accurate and reliable linear feature extraction, ext (open full item for complete abstract)

Committee: Anton F. Schenk (Advisor); Alper Yilmaz (Committee Co-Chair); Ralph R.B. von Frese (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2008, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1970, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1962, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1961, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1965, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1963, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1960, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2024, Dentistry

Purpose: To evaluate and compare the vertical gap between the milled titanium framework obtained from different impression techniques, and the abutment replica of the patient model. Materials and Methods: Four implant Simulated Patient Model was duplicated from a demonstration resin model. The implant impression of the edentulous arch was obtained using 6 techniques: Conventional splinted Open-Custom tray impression and digitized using scan bodies and bench top scanner (CNV), Intraoral scanning using manufacturer scan bodies (IOS), Intraoral scanning using Optisplint (JIGI), Benchtop scanning of the assembled Optiplint after pouring in type IV stone (JIGE), Photogrammetry using ICAM (DOM), Photogrammetry using MicronMapper (SB). N=6 for all groups. CAD/CAM titanium bar was designed directly to the MUA from all the scans (N=36) using 5 axis milling machine. Modified one-screw test was used to evaluate the fit of the bar to the Simulated Patient Model, and the gaps were measured using Nikon Measurescope MM- 1. A Pairwise analysis between the gap measurements for 6 measuring sites in 6 groups was established by performing 2-way ANOVA and post hoc Tukey test. Results: The result of the 2-way ANOVA showed that the technique and the location significantly influenced the gap size. A14 wa not consistently the largest gap among the groups. The pairwise analysis showed that CNV, DOM, and JIGE were clinically acceptable with no significant difference between the gap size. JIGE had the smallest gap size, IOS had the widest gap size. JIGI improved the quality of intraoral scanning. DOM had smaller gap size compared to SB, JIGE had significantly smaller gaps when compared to JIGI. Conclusions: Utilizing the 1-screw test did not consistently reveal the largest gap at the distal-most site. Intraoral scanning of full arch implant prosthetics using regular scan bodies consistently produced the largest measured gaps. Photogrammetry and benchtop scanning of scannable verificat (open full item for complete abstract)

Committee: Robert Seghi (Advisor); Damian Lee (Committee Member); Scott Schricker (Committee Member) Subjects: Dentistry

Master of Science, The Ohio State University, 2024, Food, Agricultural and Biological Engineering

Waste coal in abandoned mine lands poses significant environmental challenges, affecting nearby communities, rivers, and streams. Effective management of these piles is essential due to concerns such as acid mine drainage, soil and water contamination, coal fires, and methane emissions. Various strategies have been proposed for managing waste coal, including potential utilization for rare earth element recovery, soil amendment, construction aggregates, and energy generation. However, the implementation of these strategies remains uncertain due to the lack of precise location and volume data on waste coal piles. Traditional methods for gathering these data rely on field visits and Global Navigation Satellite System surveying, which are costly and labor-intensive. Advances in satellite technologies and the availability of digital elevation models (DEMs) offer an opportunity to estimate waste coal volume on a regional scale in a timely and cost-effective manner. Thus, the objective of this thesis was to develop a robust data analytical framework to locate and estimate the volume of waste coal piles on a regional scale, using the Muskingum River Basin (MRB) in Ohio as the study area. Initially, a prototype was developed to determine the most effective machine learning (ML) model to map waste coal piles in a historical coal mine site within the MRB. While all four ML models effectively identified dominant classes such as Grassland and Forest, the Random Forest (RF) model demonstrated superior performance in classifying the more complex waste coal class, with a precision of 86.15% and recall of 76.71%. Subsequently, the greatest disturbance and reclamation mapping of these waste coal piles were conducted using the LandTrendr algorithm to distinguish waste coal piles in abandoned mine lands from those in active mining areas. Moreover, this study utilized publicly available elevation models to estimate waste coal volume in the MRB. However, since historical terrain mo (open full item for complete abstract)

Committee: Ajay Shah (Advisor); Sami Khanal (Advisor); Tarunjit Singh Butalia (Committee Member) Subjects: Artificial Intelligence; Engineering; Geographic Information Science; Remote Sensing; Sustainability

Master of Arts, The Ohio State University, 2024, Geography

Drone-based remote sensing offers a practical platform for studying debris-covered ice and rock glaciers where the surface topography makes fieldwork challenging or prohibitive. Situated between the scale provided by terrestrial or satellite mapping, drones have been widely used over the past decade to construct high resolution maps of surface deformation and flow of glaciers and rock glaciers using structure from motion photogrammetry. However, ground penetrating radar (GPR) is a standard tool for studying ice within debris-covered glaciers and rock glaciers that has not seen widespread deployment in a drone-based capacity. This thesis demonstrates the application of both photogrammetry and GPR as drone-based tools to study buried ice. Fieldwork involved drone photogrammetry of the Lehman Rock Glacier (LRG) at Great Basin National Park (GBNP) in Nevada, USA and drone-based GPR of a debris-covered glacier at Shar Shaw Taga (also called Grizzly Creek) in Kluane National Park and Reserve in southwest Yukon, Canada. Drone photogrammetry of the LRG demonstrated that the upper lobe of the rock glacier is active and flowing at rates of approximately 0.2 – 0.4 m/yr at the center of the upper lobe between 2018 and 2023 with surface elevation loss in the range of 0.75 – 1.5 meters over the same period. The total volume lost is estimated to be 21,000 m3. Drone-based GPR at Shar Shaw Taga demonstrated successful detection and measurement of buried ice along the drone-based transect validated by traditional manual GPR. The inclusion of manual common mid-point (CMP) surveys enabled accurate measurements of depths and layer characteristics along the entire drone radar transect. The limitations and opportunities of the drone-based photogrammetry and GPR are discussed for potential future investigations using these methods.

Committee: Bryan Mark (Advisor); Demián Gómez (Committee Member); Ian Howat (Committee Member); Alvaro Montenegro (Committee Member) Subjects: Geography; Physical Geography

Doctor of Philosophy, The Ohio State University, 2024, Civil Engineering

With the development of remote sensing technology in recent decades, spaceborne sensors with sub-meter and meter spatial resolution (Worldview & PlanetScope) have achieved a considerable image quality to generate 3D geospatial data via a stereo matching pipeline. These achievements have significantly increased the data accessibility in 3D, necessitating adapting these 3D geospatial data to analyze human and natural environments. This dissertation explores several novel approaches based on stereo and multi-view satellite image-derived 3D geospatial data, to deal with remote sensing application issues for built-up area modeling and natural environment monitoring, including building model 3D reconstruction, glacier dynamics tracking, and lake algae monitoring. Specifically, the dissertation introduces four parts of novel approaches that deal with the spatial and temporal challenges with satellite-derived 3D data. The first study advances LoD-2 building modeling from satellite-derived Orthophoto and DSMs with a novel approach employing a model-driven workflow that generates building rectangular 3D geometry models. By integrating deep learning for building detection, advanced polygon extraction, grid-based decomposition, and roof parameter computation, we accurately computed complex building structures in 3D, culminating in the development of SAT2LoD2—a popular open-source tool in satellite-based 3D urban reconstruction. Secondly, we further enhanced our building reconstruction framework for dense urban areas and non-rectangular purposes, we implemented deep learning for unit-level segmentation and introduced a gradient-based circle reconstruction for circular buildings to develop a polygon composition technique for advanced building LoD2 reconstruction. This approach refines building 3D modeling in complex urban structures, particularly for challenging architectural forms. Our third study utilizes high-spatiotemporal resolution PlanetScope satellite imagery for (open full item for complete abstract)

Committee: Rongjun Qin (Advisor); Charles Toth (Committee Member); Alper Yilmaz (Committee Member) Subjects: Civil Engineering; Environmental Science; Geographic Information Science; Geography; Remote Sensing

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

Vibration based methods for Structural Health Monitoring (SHM) have become increasingly popular over the last few decades. In this field, non-contact measurement techniques have garnered a lot of attention due to its capacity to provide spatially dense full-field measurements. One such method is photogrammetry, which works on the technique of tracking an object or feature on the surface of a structure. This study proposes a fully non-contact method for estimating the Operational Deflection Shape (ODS) of a plate-like structure based on detecting and tracking a laser projected feature which can provide a dense measurement grid and has no permanent effect on the surface of the structure being measured. It has been long established that any local damage to a structure defined by a loss of stiffness causes local anomalies in the structure's ODS, which can be localized by taking the second derivative of the ODS, commonly known as its curvature. In this study, a damage index is formulated for plate-like structures using higher order derivatives, namely the second, fourth and sixth derivatives, with different orders of accuracy and is shown to successfully identify damage with no knowledge of the structure's undamaged state. The derivatives are computed using the central finite difference scheme. Both experimental and numerical studies are conducted to test the robustness and efficacy of the proposed index. In the numerical investigations, a Finite Element Model (FEM) is used to simulate and extract the natural frequencies and mode shapes of a plate-like structure under free-free boundary conditions. The robustness of the proposed damage index is studied for varying levels of measurement errors, simulated by adding white Gaussian noise. The effects of different parameters like the location and size of the damage are also studied. In the experimental investigation, a damaged steel plate is acoustically excited using an electric speaker at a frequency very close to o (open full item for complete abstract)

Committee: Yongfeng Xu Ph.D. (Committee Chair); Allyn Phillips Ph.D. (Committee Member); Jay Kim Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2024, Civil Engineering

3D scene modeling techniques serve as the bedrocks in the geospatial engineering and computer science, which drives many applications ranging from automated driving, terrain mapping, navigation, virtual, augmented, mixed, and extended reality (for gaming and movie industry etc.). This dissertation presents a fraction of contributions that advances 3D scene modeling to its state of the art, in the aspects of both appearance and geometry modeling. In contrast to the prevailing deep learning methods, as a core contribution, this thesis aims to develop algorithms that follow first principles, where sophisticated physic-based models are introduced alongside with simpler learning and inference tasks. The outcomes of these algorithms yield processes that can consume much larger volume of data for highly accurate reconstructing 3D scenes at a scale without losing methodological generality, which are not possible by contemporary complex-model based deep learning methods. Specifically, the dissertation introduces three novel methodologies that address the challenges of inferring appearance and geometry through physics-based modeling. Firstly, we address the challenge of efficient mesh reconstruction from unstructured point clouds—especially common in large and complex scenes. The proposed solution employs a cutting-edge framework that synergizes a learned virtual view visibility with graph-cut based mesh generation. We introduce a unique three-step deep network that leverages depth completion for visibility prediction in virtual views, and an adaptive visibility weighting in the graph-cut based surface. This hybrid approach enables robust mesh reconstruction, overcoming the limitations of traditional methodologies and showing superior generalization capabilities across various scene types and sizes, including large indoor and outdoor environments. Secondly, we delve into the intricacies of combining multiple 3D mesh models, particularly those obtained through oblique ph (open full item for complete abstract)

Committee: Rongjun Qin (Advisor); Alper Yilmaz (Committee Member); Charles Toth (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2023, Environment and Natural Resources

Urban forests are important infrastructure in cities that hope to mitigate the worst effects of urbanization and climate change. Trees are shown to remove pollution, reduce surface temperature, intercept stormwater, sequester carbon, and secure other ecosystem services (Berland et al., 2017; Escobedo & Nowak, 2009; D. J. Nowak et al., 2008, 2013; Rahman et al., 2018; Speak et al., 2020; Xiao & McPherson, 2002). These beneficial forest processes can be modeled and quantified using environmental conditions and tree characteristics (Bodnaruk et al., 2017; D. J. Nowak et al., 2008; D. J. Nowak, 2021; Rotzer et al., 2019). Among these characteristics, crown structure and leaf metrics are important factors to be quantified in efforts to estimate ecosystem services provided by urban forests (Rotzer et al., 2021a). Because crown structure and leaf metrics are so impactful for estimating ecosystem services, the accuracy of their assessments is important. However, methods to measure these characteristics exist under different levels of assumption, generalization, and uncertainty. For example, the highly heterogenous, fragmented urban forest is subject to unique anthropogenic conditions that can make cities within the same climate region distinct with regards to crown structure and leaf metrics. Such variation makes application of more generalized allometric equations, which describe how the characteristics of living trees change with size, uncertain (Berland, 2020; McHale et al., 2009a). Climate change will only exacerbate this uncertainty by making older allometric models unreliable (Pretzsch et al., 2017). In addition to the uncertainty in model application, tree crown measurements and estimation techniques often differ in dimensionality. For example, allometric models may only characterize a tree crown across its height and width while photogrammetric reconstruction can be a complex mesh made up of thousands of polygons. This difference in a model's dimensionality h (open full item for complete abstract)

Committee: Steve Lyon (Advisor); Yanlan Liu (Committee Member); Matt Lewis (Committee Member) Subjects: Environmental Science; Environmental Studies; Geographic Information Science