145 results on '"Michael J. Olsen"'
Search Results
2. Author Correction: Inversions of landslide strength as a proxy for subsurface weathering
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Stefano Alberti, Ben Leshchinsky, Josh Roering, Jonathan Perkins, and Michael J. Olsen
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Science - Published
- 2023
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3. New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans
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Jonathan L. Batchelor, Todd M. Wilson, Michael J. Olsen, and William J. Ripple
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terrestrial lidar ,TLS ,forest structure ,depth ,openness ,viewshed ,Science - Abstract
We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) point clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure of the visible distance in all directions from plot center. Openness is the percent of scan pulses in the near-omnidirectional view without a return. Isovists are a measurement of the area visible from the scan location, a quantified measurement of the viewshed within the forest canopy. 243 scans were acquired in 27 forested stands in the Pacific Northwest region of the United States, in different ecoregions representing a broad gradient in structural complexity. All stands were designated natural areas with little to no human perturbations. We created “structural signatures” from depth and openness metrics that can be used to qualitatively visualize differences in forest structures and quantitively distinguish the structural composition of a forest at differing height strata. In most cases, the structural signatures of stands were effective at providing statistically significant metrics differentiating forests from various ecoregions and growth patterns. Isovists were less effective at differentiating between forested stands across multiple ecoregions, but they still quantify the ecological important metric of occlusion. These new metrics appear to capture the structural complexity of forests with a high level of precision and low observer bias and have great potential for quantifying structural change to forest ecosystems, quantifying effects of forest management activities, and describing habitat for organisms. Our measures of structure can be used to ground truth data obtained from aerial lidar to develop models estimating forest structure.
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- 2022
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4. SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning
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Andrew Senogles, Michael J. Olsen, and Ben Leshchinsky
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landslide ,monitoring ,deep learning ,lidar ,UAS ,optical flow ,Science - Abstract
Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches.
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- 2022
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5. Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)
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Rubini Mahalingam, Michael J. Olsen, and Matt S. O'Banion
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Landslides ,LiDAR ,Oregon ,statistical techniques ,landslide inventory ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Risk in industry. Risk management ,HD61 - Abstract
Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.
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- 2016
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6. Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping
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Rubini Mahalingam and Michael J. Olsen
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Landslides ,LiDAR ,spatial resolution ,DEM ,Oregon ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Risk in industry. Risk management ,HD61 - Abstract
Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities plan and prepare for these damaging events. Digital elevation models (DEMs) are one of the most important data-sets used in landslide hazard assessment. Despite their frequent use, limited research has been completed to date on how the DEM source and spatial resolution can influence the accuracy of the produced landslide susceptibility maps. The aim of this paper is to analyse the influence of spatial resolutions and source of DEMs on landslide susceptibility mapping. For this purpose, Advanced Spaceborne Thermal Emission and Reflection (ASTER), National Elevation Dataset (NED), and Light Detection and Ranging (LiDAR) DEMs were obtained for two study sections of approximately 140 km2 in north-west Oregon. Each DEM was resampled to 10, 30, and 50 m and slope and aspect grids were derived for each resolution. A set of nine spatial databases was constructed using geoinformation science (GIS) for each of the spatial resolution and source. Additional factors such as distance to river and fault maps were included. An analytical hierarchical process (AHP), fuzzy logic model, and likelihood ratio-AHP representing qualitative, quantitative, and hybrid landslide mapping techniques were used for generating landslide susceptibility maps. The results from each of the techniques were verified with the Cohen's kappa index, confusion matrix, and a validation index based on agreement with detailed landslide inventory maps. The spatial resolution of 10 m, derived from the LiDAR data-set showed higher predictive accuracy in all the three techniques used for producing landslide susceptibility maps. At a resolution of 10 m, the output maps based on NED and ASTER had higher misclassification compared to the LiDAR-based outputs. Further, the 30-m LiDAR output showed improved results over the 10-m NED and 10-m ASTER output, indicating that finer resolution does not necessarily result in higher predictive accuracy in landslide mapping. The source of the data-sets is an important consideration and can have significant influence on the accuracy of a landslide susceptibility analysis.
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- 2016
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7. Data Gap Classification for Terrestrial Laser Scanning-Derived Digital Elevation Models
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Matthew S. O’Banion, Michael J. Olsen, Jeff P. Hollenbeck, and William C. Wright
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data gaps ,occlusions ,dropouts ,terrestrial laser scanning ,TLS ,lidar ,Geography (General) ,G1-922 - Abstract
Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions.
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- 2020
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8. A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration
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Alireza G. Kashani, Michael J. Olsen, Christopher E. Parrish, and Nicholas Wilson
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LIDAR ,laser scanning ,intensity ,normalization ,correction ,calibration ,radiometric ,Chemical technology ,TP1-1185 - Abstract
In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.
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- 2015
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9. To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection
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Michael J. Olsen, Joseph Wartman, Martha McAlister, Hamid Mahmoudabadi, Matt S. O’Banion, Lisa Dunham, and Keith Cunningham
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point cloud ,surface modeling ,laser scanning ,lidar ,change detection ,rockfalls ,geohazards ,Science - Abstract
Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitude-frequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.
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- 2015
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10. An Efficient Framework for Mobile Lidar Trajectory Reconstruction and Mo-norvana Segmentation
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Erzhuo Che and Michael J. Olsen
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feature extraction ,mobile lidar ,point cloud ,segmentation ,trajectory ,visualization ,Science - Abstract
Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the point clouds, processing MLS data can be still challenging with respect to effectiveness, efficiency, and versatility. This paper proposes an efficient MLS data processing framework for general purposes consisting of three main steps: trajectory reconstruction, scan pattern grid generation, and Mo-norvana (Mobile Normal Variation Analysis) segmentation. We present a novel approach to reconstructing the scanner trajectory, which can then be used to structure the point cloud data into a scan pattern grid. By exploiting the scan pattern grid, point cloud segmentation can be performed using Mo-norvana, which is developed based on our previous work for processing Terrestrial Laser Scanning (TLS) data, normal variation analysis (Norvana). In this work, with an unorganized MLS point cloud as input, the proposed framework can complete various tasks that may be desired in many applications including trajectory reconstruction, data structuring, data visualization, edge detection, feature extraction, normal estimation, and segmentation. The performance of the proposed procedures are experimentally evaluated both qualitatively and quantitatively using multiple MLS datasets via the results of trajectory reconstruction, visualization, and segmentation. The efficiency of the proposed method is demonstrated to be able to handle a large dataset stably with a fast computation speed (about 1 million pts/sec. with 8 threads) by taking advantage of parallel programming.
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- 2019
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11. A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives
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Michael D. Bunn, Ben A. Leshchinsky, Michael J. Olsen, and Adam Booth
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landslide inventory ,LIDAR ,Contour Connection Method ,semi automated ,Big Elk Creek ,Dixie Mountain ,Gales Creek ,Science - Abstract
Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.
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- 2019
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12. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review
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Erzhuo Che, Jaehoon Jung, and Michael J. Olsen
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point cloud ,lidar ,mobile laser scanning ,feature extraction ,segmentation ,object recognition ,classification ,Chemical technology ,TP1-1185 - Abstract
Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.
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- 2019
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13. Synthesis of Transportation Applications of Mobile LIDAR
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Keith Williams, Michael J. Olsen, Gene V. Roe, and Craig Glennie
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mobile LIDAR ,transportation ,MLS ,mobile laser scanning ,Science - Abstract
A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on “what” is being done in practice, but not a lot on “how” and “how well” it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR.
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- 2013
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14. Vo-Norvana : Versatile Framework for Efficient Segmentation of Large Point Cloud Data Sets.
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Erzhuo Che and Michael J. Olsen
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- 2023
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15. LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter.
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Andrew Senogles, Michael J. Olsen, and Ben A. Leshchinsky
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- 2023
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16. New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans.
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Jonathan L. Batchelor, Todd M. Wilson, Michael J. Olsen, and William J. Ripple
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- 2023
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17. Lidar-Derived Rockfall Inventory - An Analysis of the Geomorphic Evolution of Rock Slopes and Modifying the Rockfall Activity Index (RAI).
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Shane J. Markus, Joseph Wartman, Michael J. Olsen, and Margaret M. Darrow
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- 2023
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18. Detecting sudden moving objects in a series of digital images with different exposure times.
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Hamid Mahmoudabadi, Michael J. Olsen, and Sinisa Todorovic
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- 2017
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19. Data Gap Classification for Terrestrial Laser Scanning-Derived Digital Elevation Models.
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Matt S. O'Banion, Michael J. Olsen, Jeff P. Hollenbeck, and William C. Wright
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- 2020
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20. An Integrative Framework to Measure the Impacts of Earthquake-Induced Landslides on Transportation Network Mobility and Accessibility
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Shangjia Dong, Haizhong Wang, Michael J. Olsen, Andre R. Barbosa, and Michael D. Bunn
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- 2022
21. Contour Connection Method for automated identification and classification of landslide deposits.
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Ben A. Leshchinsky, Michael J. Olsen, and Burak F. Tanyu
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- 2015
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22. Superpixel Clustering and Planar Fit Segmentation of 3D LIDAR Point Clouds.
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Hamid Mahmoudabadi, Timothy Shoaf, and Michael J. Olsen
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- 2013
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23. Automated quantification of distributed landslide movement using circular tree trunks extracted from terrestrial laser scan data.
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Jeremy C. Conner and Michael J. Olsen
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- 2014
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24. Evaluation of Uncrewed Aircraft Systems' Lidar Data Quality.
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Benjamin J. Babbel, Michael J. Olsen, Erzhuo Che, Ben A. Leshchinsky, Chase Simpson, and Jake Dafni
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- 2019
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25. Terrestrial Laser Scanning
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Michael J. Olsen
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- 2022
26. Mobile Terrestrial Laser Scanning and Mapping
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Michael J. Olsen, Jaehoon Jung, Erzhuo Che, and Chris Parrish
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- 2022
27. Efficient segment-based ground filtering and adaptive road detection from mobile light detection and ranging (LiDAR) data
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Erzhuo Che, Jaehoon Jung, and Michael J. Olsen
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010504 meteorology & atmospheric sciences ,Light detection ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0211 other engineering and technologies ,Ranging ,02 engineering and technology ,01 natural sciences ,Lidar ,General Earth and Planetary Sciences ,Lidar data ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Mobile light detection and ranging (LiDAR) has been widely applied to support a variety of tasks because it captures detailed three-dimensional data of a scene with high accuracy with reduced costs...
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- 2021
28. Using High Sample Rate Lidar to Measure Debris-Flow Velocity and Surface Geometry
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Joel B. Smith, Kate E. Allstadt, Thomas Rapstine, Ben Leshchinsky, Maciej K. Obryk, Francis K. Rengers, Michael J. Olsen, and Richard M. Iverson
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Environmental Engineering ,Lidar ,010504 meteorology & atmospheric sciences ,Earth and Planetary Sciences (miscellaneous) ,Measure (physics) ,Surface geometry ,010502 geochemistry & geophysics ,Geotechnical Engineering and Engineering Geology ,01 natural sciences ,Geology ,0105 earth and related environmental sciences ,Remote sensing ,Debris flow - Abstract
Debris flows evolve in both time and space in complex ways, commonly starting as coherent failures but then quickly developing structures such as roll waves and surges. These processes are readily observed but difficult to study or quantify because of the speed at which they evolve. Many methods for studying debris flows consist of point measurements (e.g., flow height or basal stresses), which are inherently limited in spatial coverage and cannot fully characterize the spatiotemporal evolution of a flow. In this study, we use terrestrial lidar to measure debris-flow profiles at high sampling rates to examine debris-flow movement with high temporal and spatial precision and accuracy. We acquired measurements during gate-release experiments at the U.S. Geological Survey debris-flow flume, a unique experimental facility where debris flows can be artificially generated at a large scale. A lidar scanner was used to record repeat topographic profiles of the moving debris flows along the length of the flume with a narrow swath width (∼1 mm) at a rate of 60 Hz. The high-resolution lidar profiles enabled us to quantify flow front velocity of the debris flows and provided an unprecedented record of the development and evolution of the flow structure with a sub-second time resolution. The findings of this study demonstrate how to obtain quantitative measurements of debris-flow movement. In addition, the data help us to quantitatively define the development of a saltating debris-flow front and roll waves behind the debris-flow front. Such measurements may help constrain future modeling efforts.
- Published
- 2021
29. TopCAT - Topographical Compartment Analysis Tool to analyze seacliff and beach change in GIS.
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Michael J. Olsen, Adam P. Young, and Scott A. Ashford
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- 2012
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30. Terrestrial Laser Scanning-Based Structural Damage Assessment.
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Michael J. Olsen, Falko Kuester, Barbara J. Chang, and Tara C. Hutchinson
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- 2010
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31. The Hooskanaden Landslide: historic and recent surge behavior of an active earthflow on the Oregon Coast
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Adam M. Booth, Curran Mohney, Andrew Senogles, S. Alberti, Ben Leshchinsky, Pete Castro, Jill DeKoekkoek, Kara Kingen, Michael J. Olsen, and Kira Glover-Cutter
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021110 strategic, defence & security studies ,Earthflow ,Baseline (sea) ,0211 other engineering and technologies ,Landslide ,02 engineering and technology ,Geotechnical Engineering and Engineering Geology ,Pacific ocean ,Natural hazard ,Surge ,Maximum displacement ,Seismology ,Geology ,021101 geological & geomatics engineering - Abstract
This paper presents an analysis of the Hooskanaden Landslide, an earthflow, which experienced a dramatic surge event beginning on February 24, 2019, closing US Highway 101 near mile point 343.5 for nearly 2 weeks. This ~ 1 km long surge event resulted in horizontal displacements of up to 45 m and uplift of 6 m at the toe located on a gravel beach adjacent to the Pacific Ocean. The Hooskanaden Landslide, likely active since the eighteenth century, exhibits regular activity with a recurrence interval of major surge events of approximately every 20 years, transitioning from slow to relatively rapid velocities. During the 2019 event, maximum displacement rates of approximately 60 cm/h were observed, slowly decreasing to 15 cm/h for a sustained period of approximately 2 weeks before the eventual return to baseline conditions (
- Published
- 2020
32. A Geotechnical Database for Utah (GeoDU) enabling quantification of geotechnical properties of surficial geologic units for geohazard assessments
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Mahyar Sharifi-Mood, Daniel T. Gillins, Kevin W. Franke, Michael J. Olsen, and Steven F. Bartlett
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Hazard mapping ,021110 strategic, defence & security studies ,Geophysics ,0211 other engineering and technologies ,Borehole ,Liquefaction ,Geotechnical engineering ,02 engineering and technology ,Geohazard ,Geotechnical Engineering and Engineering Geology ,Geology ,021101 geological & geomatics engineering - Abstract
Geotechnical borehole information is often used for liquefaction hazard mapping, but can be highly variable in terms of quantity and quality. In addition, geotechnical borehole logs are often provided as images in reports rather than delivered in a structured, queryable database, which makes the logs and supplementary information difficult to organize particularly across a large geographic area. In contrast, surficial geologic mapping is generally available and often accessible in geographic information systems (GIS) format. This article’s objective is to describe the compilation of a geotechnical database for regional mapping purposes and to demonstrate the value of documenting geotechnical data into a consistent data format. Specifically, this article describes the development of three geotechnical borehole databases compiled in Utah, which has been coined the Geotechnical Database for Utah (GeoDU). The database is used to quantify geotechnical properties for subsequent liquefaction evaluations of surficial geologic units comprising similar depositional environment and age. The resulting GeoDU is an important resource for future efforts with many applications including community data sharing and planning for preliminary geotechnical site investigations.
- Published
- 2020
33. Policy processes and recommendations for Unmanned Aerial System operations near roadways based on visual attention of drivers
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Hisham Jashami, Alden Sova, Zachary Barlow, Michael J. Olsen, and David S. Hurwitz
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050210 logistics & transportation ,Government ,Emerging technologies ,Computer science ,05 social sciences ,Driving simulator ,Poison control ,Transportation ,010501 environmental sciences ,Management Science and Operations Research ,01 natural sciences ,Drone ,Aeronautics ,restrict ,0502 economics and business ,Automotive Engineering ,Visual attention ,Recreation ,0105 earth and related environmental sciences ,Civil and Structural Engineering - Abstract
Unmanned Aerial Systems (UASs), commonly known as drones, are a rapidly emerging technology with many applications across various commercial, government, and recreational users. Many of these applications have the potential to interact with roadway infrastructure, resulting in potentially risky conflicts between UAS operations and drivers on the roadway. In the United States, policy regulating UAS operations exists at the federal, state, and local levels, but there is little to no regulation specifically related to UAS operations near roadways. The purpose of this study was to evaluate if UAS operations near roadways pose a safety concern by determining if the operations visually distract drivers. In addition, this study sought to develop data-driven policy recommendations to improve the safety of drivers and UAS operators near roadways. To understand how UAS operations near roadways influence the visual attention of drivers, an experiment was designed and conducted in a high-fidelity driving simulator. Thirty participants completed the experiment in the driving simulator and their visual attention was recorded. Analysis of the visual attention results showed that UAS operations draw more visual attention from drivers when they are directly adjacent to the roadside or in a rural environment. Based on the results, a recommended policy to improve safety of UASs for operators and drivers would be to, at a minimum, restrict UAS operations within 7.6 m (25 ft) of the edge of a lane. A procedural overview for implementing legal and effective UAS policy in the United States was developed to navigate the complexities of the evolving UAS policy landscape.
- Published
- 2019
34. Capturing Geotechnical Extreme Event Performance with the NHERI RAPID
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Michael J. Grilliot, Joseph Wartman, Michael J. Olsen, Ann Bostrom, Andrew Lyda, Laura N. Lowes, Jennifer L. Irish, Troy Tanner, Scott B. Miles, Kurtis R. Gurley, Jaqueline Peltier, and Jeffrey W. Berman
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Event (relativity) ,Forensic engineering ,Environmental science - Published
- 2021
35. In Situ Change Analysis and Monitoring through Terrestrial Laser Scanning.
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Michael J. Olsen
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- 2015
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36. VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION
- Author
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Michael J. Olsen, A. Senogles, and Erzhuo Che
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Technology ,Laser scanning ,business.industry ,Computer science ,Point cloud ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Filter (signal processing) ,Engineering (General). Civil engineering (General) ,TA1501-1820 ,Lidar ,Photogrammetry ,Structure from motion ,Applied optics. Photonics ,Computer vision ,Point (geometry) ,Artificial intelligence ,TA1-2040 ,business ,Cluster analysis - Abstract
Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.
- Published
- 2021
37. Multihazard Damage and Loss Assessment of Bridges in a Highway Network Subjected to Earthquake and Tsunami Hazards
- Author
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Haizhong Wang, Michael J. Olsen, Patrick Burns, and Andre R. Barbosa
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Fragility ,Forensic engineering ,General Social Sciences ,Building and Construction ,Ground failure ,Bridge (interpersonal) ,Geology ,General Environmental Science ,Civil and Structural Engineering - Abstract
The objective of this study is to examine and compare the vulnerabilities of highway bridges to the combined effects of earthquake shaking, ground failure, and tsunami loading. Earthquake-i...
- Published
- 2021
38. Lateral spreading within a limit equilibrium framework: Newmark's sliding blocks with degrading yield accelerations
- Author
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D. T. Gillins, Sanjay Kumar Shukla, Ben Leshchinsky, Michael J. Olsen, H. Benjamin Mason, and Pragyan Pradatta Sahoo
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021110 strategic, defence & security studies ,Earthquake engineering ,Yield (engineering) ,business.industry ,0211 other engineering and technologies ,Liquefaction ,02 engineering and technology ,Structural engineering ,Geotechnical Engineering and Engineering Geology ,Soil structure interaction ,Earth and Planetary Sciences (miscellaneous) ,Geotechnical engineering ,Limit (mathematics) ,business ,Geology ,021101 geological & geomatics engineering - Abstract
Lateral spreading is a prevalent geotechnical problem associated with earthquake-induced liquefaction, often occurring at gentle slopes of loose, saturated sand near bodies of water and causing sig...
- Published
- 2020
39. Pavement Marking Retroreflectivity Estimation and Evaluation using Mobile Lidar Data
- Author
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Erzhuo Che, Michael J. Olsen, Jaehoon Jung, and Christopher Parrish
- Subjects
Estimation ,Mobile lidar ,Environmental science ,Computers in Earth Sciences ,Remote sensing - Published
- 2019
40. Quantifying the Sensitivity of Progressive Landslide Movements to Failure Geometry, Undercutting Processes and Hydrological Changes
- Author
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Reilly McClung, Michael J. Olsen, Michael Bunn, Curran Mohney, Matthew S. O’Banion, Ben Leshchinsky, and Jonathan C. Allan
- Subjects
Geophysics ,Slope stability ,Erosion ,Geotechnical engineering ,Landslide ,Sensitivity (control systems) ,Geology ,Earth-Surface Processes - Published
- 2019
41. Efficient and robust lane marking extraction from mobile lidar point clouds
- Author
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Jaehoon Jung, Erzhuo Che, Christopher Parrish, and Michael J. Olsen
- Subjects
010504 meteorology & atmospheric sciences ,business.industry ,Computer science ,Coordinate system ,0211 other engineering and technologies ,Point cloud ,Image processing ,02 engineering and technology ,Image segmentation ,RANSAC ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,Computer Science Applications ,Robustness (computer science) ,Road surface ,Computer vision ,Artificial intelligence ,Computers in Earth Sciences ,business ,Engineering (miscellaneous) ,Opening ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro-reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types.
- Published
- 2019
42. Influence of both anisotropic friction and cohesion on the formation of tension cracks and stability of slopes
- Author
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T. Matthew Evans, Ezra Stockton, Ben Leshchinsky, and Michael J. Olsen
- Subjects
Materials science ,Isotropy ,0211 other engineering and technologies ,Geology ,02 engineering and technology ,Mechanics ,Slip (materials science) ,010502 geochemistry & geophysics ,Geotechnical Engineering and Engineering Geology ,01 natural sciences ,Friction angle ,Slope stability ,Ultimate tensile strength ,Cohesion (geology) ,Anisotropy ,Logarithmic spiral ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
A modified logarithmic-spiral limit equilibrium procedure is proposed to rigorously determine the critical failure mechanism and stability of slopes comprised of soils where both friction angle and cohesion demonstrate anisotropic shear strength. The influence of both cohesion and friction anisotropy has not been explicitly considered using the logarithmic spiral approach. This procedure allows for the critical failure mechanism and stability to be determined in simplified soil strata independent of the internal statical assumptions associated with other rigorous slope stability approaches. The unique geometry of the logarithmic spiral enables direct assessment of the normal stress distribution acting along the failure surface using a modified analytical solution. This computed normal stress distribution enables evaluation of tensile stresses along a slip surface, thus providing a rational means of explicitly determining the depth of the tension crack for both isotropic and anisotropic conditions, which is novel. Within this study, a procedure is proposed to study the influence of cohesive and frictional anisotropy on tension crack depth and slope stability. In addition, to understand the impact of an often-realistic variable on anisotropic stability, the orientation of the plane of weakness due to geologic conditions is studied over the range from 90° to −90°.
- Published
- 2019
43. Dense Point Cloud Quality Factor as Proxy for Accuracy Assessment of Image-Based 3D Reconstruction
- Author
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Michael J. Olsen, Farid Javadnejad, Daniel T. Gillins, Richard K. Slocum, and Christopher Parrish
- Subjects
010504 meteorology & atmospheric sciences ,business.industry ,Computer science ,3D reconstruction ,0211 other engineering and technologies ,Point cloud ,02 engineering and technology ,01 natural sciences ,Proxy (climate) ,Stereopsis ,Structure from motion ,Computer vision ,Artificial intelligence ,business ,Image based ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Civil and Structural Engineering - Abstract
Photogrammetry using structure from motion (SfM) and multiview stereopsis (MVS) techniques can recover three-dimensional (3D) structure from a set of overlapping, unoriented, and uncalibrat...
- Published
- 2021
44. Approximations, Errors, and Misconceptions in the Use of Map Projections
- Author
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Sergio Baselga and Michael J. Olsen
- Subjects
Geospatial analysis ,Article Subject ,Computer science ,General Mathematics ,General Engineering ,Geodetic datum ,Grid ,computer.software_genre ,Engineering (General). Civil engineering (General) ,Photogrammetry ,Intersection ,QA1-939 ,Data mining ,TA1-2040 ,Map projection ,Scale (map) ,computer ,Distance from a point to a line ,Mathematics - Abstract
Global geodetic techniques currently can provide the user with worldwide millimeter accuracy. Preservation of this degree of accuracy in derived products is far from straightforward and may leave vast room for trouble in the different steps involved in the collection, storing, processing, analysis, and delivering of geospatial information. This paper is envisioned to serve as a guide for those utilizing map projections, in any possible form of application-cartography, GIS, remote sensing, photogrammetry, etc., to the common (and not so common) causes of error and misconception. This work also explores and questions the validity of some of approximations that are routinely implemented and quantifies the corresponding impact. These include the impact of neglecting meteorological corrections, reduction to ellipsoid and grid scale factors for distances, meridian convergence and arc-to-chord correction for angles, and mixing up with different frames and reference systems, height systems, or deceptively similar map projections. Correct indications are also given for accurately performing geospatial operations such as intersection of lines, determination of minimum point to line distance, and area determination for cadaster, which are often performed with suboptimal accuracy.
- Published
- 2021
- Full Text
- View/download PDF
45. Frontiers in Built Environment
- Author
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Andrew Lyda, Kurtis R. Gurley, Michael J. Olsen, Troy Tanner, Michael J. Grilliot, Laura N. Lowes, Joseph Wartman, Ann Bostrom, Jennifer L. Irish, Scott B. Miles, Jaqueline Peltier, Jeffrey W. Berman, Jake Dafni, Center for Coastal Studies, and Civil and Environmental Engineering
- Subjects
Process management ,media_common.quotation_subject ,Geography, Planning and Development ,0211 other engineering and technologies ,020101 civil engineering ,02 engineering and technology ,natural hazard ,Field (computer science) ,0201 civil engineering ,lcsh:HT165.5-169.9 ,Natural hazard ,Instrumentation (computer programming) ,Adaptation (computer science) ,media_common ,instrumentation ,021110 strategic, defence & security studies ,Government ,Teamwork ,Community resilience ,Data collection ,reconnaissance ,Building and Construction ,lcsh:City planning ,simulation ,Urban Studies ,data ,lcsh:TA1-2040 ,disaster ,Business ,lcsh:Engineering (General). Civil engineering (General) ,Simulation - Abstract
Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines. U.S. National Science FoundationNational Science Foundation (NSF) [1611820] The U.S. National Science Foundation supported this work under grant number 1611820. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Published
- 2020
46. Natural Hazards Reconnaissance With the NHERI RAPID Facility
- Author
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Ann Bostrom, Andrew Lyda, Kurtis R. Gurley, Michael J. Grilliot, Laura N. Lowes, Jaqueline Peltier, Jeffrey W. Berman, Jennifer L. Irish, Troy Tanner, Jacob Dafni, Joseph Wartman, Michael J. Olsen, Scott B. Miles, and Civil and Environmental Engineering
- Subjects
Engineering ,Data collection ,business.industry ,reconnaissance ,Geography, Planning and Development ,field data collection ,Building and Construction ,Plan (drawing) ,lcsh:City planning ,Urban Studies ,research instrumentation ,lcsh:HT165.5-169.9 ,Engineering management ,Cyberinfrastructure ,natural hazards ,lcsh:TA1-2040 ,Natural hazard ,Portfolio ,Instrumentation (computer programming) ,Resilience (network) ,business ,Engineering research ,lcsh:Engineering (General). Civil engineering (General) ,lidar - Abstract
In 2016, the National Science Foundation (NSF) funded a multi-institution interdisciplinary team to develop and operate the Natural Hazards Reconnaissance Facility (known as the "RAPID") as part of the Natural Hazards Engineering Research Infrastructure (NHERI) program. During the following 2 years, the RAPID facility developed its instrumentation portfolio and operational plan with input from the natural hazards community, the facility's leadership team, and an external steering committee. In September 2018, the RAPID began field operations, which continue today and include instrumentation, software, training, and support services to conduct reconnaissance research before, during, and after natural hazard and disaster events. Over the past 2 years, the RAPID has supported the data collection efforts for over 60 projects worldwide. Projects have spanned a wide range of disciplines and hazards and have also included data collection at large-scale experimental facilities in the United States and abroad. These projects have produced an unprecedented amount of high-quality field data archived on the DesignSafe cyberinfrastructure platform. This paper describes the RAPID facility's development, instrumentation portfolio (including the mobile application RApp), services and capabilities, and training activities. Additionally, overviews of three recent RAPID-supported projects are presented, including descriptions of field data collection workflows, details of the resulting data sets, and the impact of these project deployments on the natural hazard fields. NSFNational Science Foundation (NSF) [1904653, 1904327, CMMI: 1611820]; NSF through GEER [1826118]; Oregon DOT; FHWA [SPR807] The RAPID Facility operates under a cooperative agreement with the NSF under Award No. CMMI: 1611820. Research on the performance of LRLVBs in Hurricane Michael was supported by the NSF under award nos. 1904653 and 1904327. Research on the flow slide during the Palu, Indonesia earthquake was supported by the NSF through GEER under award number 1826118. Any opinions, findings, conclusions, and recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation. Funding for the Hooskadaden Landslide case study were provided by Oregon DOT and FHWA (SPR807).
- Published
- 2020
47. VR-based visual analytics of LIDAR data for cliff erosion assessment.
- Author
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Tung-Ju Hsieh, Michael J. Olsen, Elizabeth Johnstone, Adam P. Young, Neal Driscoll, Scott A. Ashford, and Falko Kuester
- Published
- 2007
- Full Text
- View/download PDF
48. Geologic Trends in Shear Strength Properties Inferred Through Three‐Dimensional Back Analysis of Landslide Inventories
- Author
-
Michael Bunn, Michael J. Olsen, and Ben Leshchinsky
- Subjects
Geophysics ,Back analysis ,Slope stability ,Shear strength ,Geotechnical engineering ,Landslide ,Geology ,Earth-Surface Processes - Published
- 2020
49. Volume Characteristics of Landslides Triggered by the M W 7.8 2016 Kaikōura Earthquake, New Zealand, Derived From Digital Surface Difference Modeling
- Author
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Katie Jones, Dougal Townsend, David N. Petley, Biljana Lukovic, David A. Rhoades, Regine Morgenstern, W. Ries, Nicola Litchfield, Jamie Howarth, Michael J. Olsen, Ian Hamling, Joseph Wartman, Marin K. Clark, Brenda Rosser, and Chris Massey
- Subjects
Geophysics ,Volume (thermodynamics) ,Landslide ,Digital surface ,Geomorphology ,Geology ,Earth-Surface Processes - Published
- 2020
50. Limit Equilibrium Stability Analysis of Layered Slopes: a Generalized Approach
- Author
-
Yonggui Xie, Ezra Stockton, Michael J. Olsen, Ben Leshchinsky, and Dov Leshchinsky
- Subjects
Environmental Engineering ,Normal force ,Shear force ,Mathematical analysis ,0211 other engineering and technologies ,Transportation ,02 engineering and technology ,Slip (materials science) ,Geotechnical Engineering and Engineering Geology ,Finite element method ,Factor of safety ,021105 building & construction ,Limit state design ,Statics ,Logarithmic spiral ,021101 geological & geomatics engineering ,Civil and Structural Engineering ,Mathematics - Abstract
Traditionally, application of the conventional logarithmic spiral in limit equilibrium (LE) analyses has been limited to homogenous materials. Herein, a modification of the conventional logarithmic spiral LE approach is proposed to account for transitions in soil conditions and provide insight into the internal statics associated with this approach, termed the compound log-spiral (CLS). Comparing both factor of safety (FS) and failure surfaces for a range of frictional strength combinations, the CLS approach demonstrates good agreement with results derived from both generalized, commercially available rigorous LE analyses and finite element analyses. The utility of the CLS method is further demonstrated through an example where the stability of a heavily stratified seacliff is considered. The proposed method satisfies equilibrium at a limit state without resorting to internal statistical assumptions associated with traditional LE approaches. Furthermore, it enables the explicit determination of internal statics, such as inter-slice shear forces, inter-slice normal forces, inter-slice angle, line of thrust, and normal stress distributions, which is a less-than-trivial task for the complex slip surface geometry realized in heterogeneous slope failures. Subsequently, the reasonableness of results could be assessed.
- Published
- 2018
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