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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...

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.

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 (

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.

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.

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...

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...

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.

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°.

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.

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...

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.

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.

Point cloud segmentation groups points with similar attributes with respect to geometric, colormetric, radiometric, and/or other information to support Terrestrial Laser Scanning (TLS) data processing such as feature extraction, classification, modeling, analysis, and so forth. In this paper we propose a segmentation method consisting of two main steps. First, a novel feature extraction approach, NORmal VAriation ANAlysis (Norvana), eliminates some noise points and extracts edge points without requiring a general (and error prone) normal estimation at each point. Second, region growing groups the points on a smooth surface to obtain the segmentation result. For efficiency, both steps exploit the angular grid structure storing each TLS scan that is often neglected in many segmentation algorithms, which are primarily developed for unorganized point clouds. Additionally, unlike the existing methods exploiting the angular grid structure that can only be applied to a single scan, the proposed method is able to segment multiple registered scans simultaneously. The algorithm also takes advantage of parallel programming for efficiency. In the experiment, both qualitative and quantitative evaluations are performed through two datasets whilst the robustness and efficiency of the proposed method are analyzed and discussed.

Earthquake-induced liquefaction may result in the lateral spread displacement of soil down gently sloping ground or towards a free-face, causing severe and costly damage to various facilities, bridges, buildings and other critical infrastructure. Despite the availability of analytical methods, most engineers currently use empirical or semi-empirical regression models to estimate liquefaction-induced lateral spread displacements at specific sites. However, the application of these regression models for regional mapping over a large geographic areas can be difficult because of challenges associated with the adequate characterization of subsurface soil and groundwater conditions, geotechnical properties, regional topography, and uncertainties associated with the causative seismic loading. To address these challenges, this paper presents a new and fully probabilistic procedure for regional hazard mapping of liquefaction-induced lateral spread displacement. The mapping process is demonstrated through an implementation in Utah County, Utah. To demonstrate the type of lateral spread displacement hazard maps possible, maps corresponding to return periods of 1033 and 2475 years are developed for Utah County, Utah. The proposed procedure incorporates topographical data from airborne lidar surveys and geotechnical and geological data from available maps and subsurface explorations. It accounts for uncertainties in the soil properties, seismic loading, and the empirical models for predicting lateral spread displacement using Monte Carlo simulations.

Sight distance analyses require careful and detailed field measurements to facilitate proper engineering decision making regarding the removal of obstructions, establishment of regulatory and advisory speed limits, and the location of new access points, among numerous other purposes. However, conventional field measurements for these analyses present safety concerns because they require personnel to be in or adjacent to traffic lanes. They can also be time consuming, costly, and labor intensive. Furthermore, the predominantly two-dimensional (2D) methods involve simplifying assumptions such as a “standard” vehicle heights and lengths without considering the wide range of vehicles and drivers present on the road. Recently, many transportation agencies worldwide have begun to acquire mobile lidar data to map their roadway assets. These data provide a rich three-dimensional (3D) environment that enables one to virtually visit a site at any frequency and efficiently evaluate sight distances from the safety of the office. This study investigates advanced safety analysis methodologies for drivers’ sight distance based on high resolution lidar data. The developed simulation method enables users to virtually evaluate available sight distances in a 3D context considering a variety of objects, vehicle types, and multi-modal forms of transportation (e.g., bicycle, pedestrian). The feasibility of this technique was analyzed with a case study at an intersection located in Corvallis, Oregon, USA. The experimental results demonstrated the ability of the proposed methodology to capture significantly more detail on visibility constraints when compared with conventional measurements as well as provide more flexibility in the analysis.

Following the 25 April 2015 Mw 7.8 Gorkha, Nepal, earthquake and subsequent aftershocks, field surveys were conducted on medium-to-high rise reinforced concrete (RC) frame buildings with masonry infill located in the Kathmandu Valley. Rapid visual assessment, ambient vibration testing, and ground-based lidar (GBL) showed that these buildings suffered damage ranging from light to severe, where damage occurred in both structural and nonstructural elements, but was most prevalent in nonstructural masonry infills. Finite-element structural analyses of selected buildings corroborate field observations of only modest structural damage. The lack of severe structural damage in this relatively limited class of engineered medium-to-high rise RC infill frame buildings illustrates the impact of modern seismic design standards and stands in stark contrast to the severe damage and collapse observed in low-rise nonengineered RC infill frame buildings. Nonetheless, the nonstructural damage hindered many of these buildings from being occupied for many months following the earthquake and subsequent aftershocks.

Seismically-induced landslides can be detrimental to urban communities, often resulting in significant damage and repair costs, blockage of lifeline connection routes and utilities, environmental impacts, and potential for loss of life. Consistent, reliable hazard maps can assist agencies to efficiently allocate limited resources to prepare for these events. This paper presents methodology for evaluating and mapping seismically-induced landslide hazards across a large area utilizing performance-based design strategies. This approach scales site-specific seismic hazard curve analysis techniques to a regional scale evaluation by combining generally available data, including: previous landslide inventories, lidar and photogrammetric topographic data, geologic mapping, NEHRP site classifications based on shear wave velocity (VS30) measurements, and seismic hazard curves for the analysis. These maps can be combined with maps generated for other hazards (e.g., liquefaction) for a fully probabilistic, multi-hazard evaluation and risk assessment. To demonstrate the methodology, a series of landslide hazard maps showing the probabilities of exceeding different thresholds of movement (e.g., 0.1, 0.3, and 1.0 m) were generated for western Oregon. The study area contains weak, wet soils that experience land sliding regularly even without significant seismic activity. The maps show reasonable agreement with landslide inventory and susceptibility maps.

This paper presents a novel methodology to combine ambient vibration-based operation modal analysis with three-dimensional ground-based lidar data to study damage on the Nyatapola Temple, which is a Bhaktapur UNESCO World Heritage Site that was damaged during the 2015 Gorkha, Nepal, earthquake. The post-earthquake ambient vibration data, collected via accelerometers placed on various levels of the temple, are used to estimate the vibrational properties via operational modal analysis. These properties are then compared to the pre-earthquake dynamic characteristics collected in 2002. The lidar data provide a geometric assessment of the current condition of the temple, capturing post-earthquake drift as a function of height as well as significant cracks present in the facade. The lidar data also inform the numerical models implemented for the post-earthquake condition assessment of the temple.

Rockfalls and debris avalanches triggered by earthquakes during the 2010–2011 Canterbury earthquake sequence killed five people and caused an estimated US$400 million in damages. In total, about 200 dwellings were directly impacted by some of the ~6000 rockfalls and debris avalanches that occurred across the Port Hills of Christchurch. This research presents the results of the analysis of a high-quality database of 61 individual rockfall impacts on 29 dwellings in the Port Hills of Christchurch, New Zealand. Dwellings in the Port Hills are typically simple timber-frame structures with wooden or unreinforced masonry cladding, comparable to most dwellings across New Zealand, North America, Australia, and elsewhere. Rockfall impacts on dwellings in this study were observed to follow a power law relationship between kinetic energy and (1) the runout distance into and through the dwelling and (2) the impacted area within the dwelling. The results have been quantified and are presented as a damage proportion, which is defined as the proportion of the area affected by an individual rock block inside the dwelling divided by the total area of the dwelling. These data provide a fundamental input for rockfall risk analysis and will allow the losses from rockfall impacts to be better constrained.

Terrestrial Laser Scanning (TLS) efficiently collects 3D information based on lidar (light detection and ranging) technology. TLS has been widely used in topographic mapping, engineering surveying, forestry, industrial facilities, cultural heritage, and so on. Ground filtering is a common procedure in lidar data processing, which separates the point cloud data into ground points and non-ground points. Effective ground filtering is helpful for subsequent procedures such as segmentation, classification, and modeling. Numerous ground filtering algorithms have been developed for Airborne Laser Scanning (ALS) data. However, many of these are error prone in application to TLS data because of its different angle of view and highly variable resolution. Further, many ground filtering techniques are limited in application within challenging topography and experience difficulty coping with some objects such as short vegetation, steep slopes, and so forth. Lastly, due to the large size of point cloud data, operations such as data traversing, multiple iterations, and neighbor searching significantly affect the computation efficiency. In order to overcome these challenges, we present an efficient ground filtering method for TLS data via a Scanline Density Analysis, which is very fast because it exploits the grid structure storing TLS data. The process first separates the ground candidates, density features, and unidentified points based on an analysis of point density within each scanline. Second, a region growth using the scan pattern is performed to cluster the ground candidates and further refine the ground points (clusters). In the experiment, the effectiveness, parameter robustness, and efficiency of the proposed method is demonstrated with datasets collected from an urban scene and a natural scene, respectively.

The method detects moving objects in a set of digital images with various exposures.The method does not rely on camera response function, object detection, reference image.It is a per-image, per-patch, adaptive approach functional in a wide range of scenes.It detects continuous or sudden movements of small or large objects within a scene.It can be further developed for object tracking within different frames of a video. Display Omitted This paper presents an algorithm to detect sudden objects appearing within a set of digital images obtained at different exposures to create a high dynamic range (HDR) image. While some previous work has focused on detecting moving objects within a scene, the majority cannot handle exposure variability. The few techniques developed specifically for HDR images track moving objects that have smaller movements within a scene compared to an abrupt object that appears and disappears quickly. Further, the algorithm advances existing methods because it does not require: 1) robust estimation of a camera response function, 2) supervision of objects in the scene such as explicit object detection and tracking, and 3) selection of a reference image. In this approach, every image in the set is first partitioned into equal size patches. Next, the properties (e.g., histograms of oriented gradients, HOG) of values within the window of the same patch are compared between the images to identify differences. Finally, a statistical classifier is developed to recognize significant differences between patch descriptors and identify patches containing sudden objects. This statistical classifier makes it possible to define confidence levels for categorizing patches into a moving object or not. A sensitivity analysis indicated that the best performance occurs when using four or six digital images. However, the optimal patch size is dependent on the size of the moving object to be detected. Hence, a mechanism is introduced to estimate the range of reasonable patch sizes given an image.

In this paper, we introduce the Rockfall Activity Index (RAI), a point cloud-derived, high-resolution, morphology-based approach for assessing rockfall hazards. With the RAI methodology, rockfall hazards are evaluated in a two-step procedure. First, morphological indices (local slope and roughness) are used to classify mass wasting processes acting on a rock-slope. These classifications are then used with estimated instability rates to map rockfall activity across an entire slope face. The rockfall hazard is quantified as the estimated annual kinetic energy produced by rockfall along 1-m length segments of a slope face. Field assessment of the RAI method at multiple study sites indicates that the morphology-derived classification and hazard assessment routines provide results that closely match the observed behavior and performance of rock slopes.

Shallow landslides are a prevalent concern in mountainous or hilly regions that can result in severe societal, economic, and environmental impacts. The challenge is further compounded as the size and location of a potential slide is often unknown. This study presents a generalized approach for the evaluation of regional shallow landslide susceptibility using an existing shallow landslide inventory, remote sensing data, and various geotechnical scenarios. The three-dimensional limit equilibrium model derived in this study uses a raster-based approach that uniquely integrates tree root reinforcement, earth pressure boundary forces, and pseudo-static seismic accelerations. Contributions of this methodology include the back-calculation of soil strength from a landslide inventory, sensitivity analyses regarding landslide size-pixel size relationships, and the determination of shallow landslide susceptibility for a landscape or infrastructure considering various root, water, and seismic conditions using lidar bare-earth DEMs as a topographic input. Using a distribution of inventoried landslide points and random points in non-landslide locales, the proposed methodology demonstrated reasonable correlation between regions of high landslide susceptibility and observed shallow landslides within a 150-km2 region of the Oregon Coast Range when the water-height ratio was 0.5. The method may be improved by considering spatial hydrologic conditions and geology more explicitly.

There is increasing recognition of the potential of Building Information Modelling (BIM) in the Architecture, Engineering and Construction (AEC) industry. With the growing use and adoption of BIM, ...

Olsen, M.J.; Johnstone, E.; Driscoll, N.; Kuester, F., and Ashford, S.A., 2016. Fate and transport of seacliff failure sediment in southern California. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 185–199. Coconut Creek (Florida), ISSN 0749-0208. Continual erosion and collapse of unstable seacliffs along the economically important coastline of San Diego County, California, threatens existing development and public safety. Frequent time-series mapping of the seacliffs and beaches provides valuable insight into the processes responsible for cliff erosion and into the reworking and transport of the failed material. High-resolution terrestrial laser scan (TLS) data provide quantitative data for analyzing seacliff morphology, capturing patterns over time and across a wide range of spatial scales. Through an ongoing “rapid response” program operat...

Johnstone, E.; Raymond, J.; Olsen, M.J., and Driscoll, N., 2016. Morphological expressions of coastal cliff erosion processes in San Diego County. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 174–184. Coconut Creek (Florida), ISSN 0749-0208. High-resolution, Terrestrial Laser Scanning (TLS) data have been acquired seasonally since 2006 to define the style and magnitude of cliff erosion along the southern 20 km of coastline within the Oceanside Littoral Cell (OLC). In particular, twelve sites with cliff collapses were mapped repeatedly to examine how these collapses propagate along the cliffs and to identify feedback mechanisms between the liberated material and subsequent cliff failures. Grain size analyses of the failed material (retention cutoff) were performed to estimate the contribution to the beach sand inventory. Despite a relatively ...

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.

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

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.

Deep-seated landslides cause significant damage to the built environment. Characterizing the shear surface and volume of these features is often limited to intensive site-specific monitoring, or at a larger scale, generalized empirical relationships developed from change analysis. However, there is limited means of generalizing regional-scale rupture surface information for deep-seated landslides where deposits still overlay much of the failure surface. Herein, a method is proposed that uses high-resolution bare earth digital elevation models with an inventory of mapped landslide extents to gather first order estimates of three-dimensional rupture surface geometries and volumes for rotational and compound landslide features at a regional scale. This approach is first calibrated to a suite of well-characterized landslides and then applied to over eight hundred deep-seated landslides in three different geologic regimes. Estimates of landslide area-volume relationships are proposed that are consistent with previous empirical assessments. The aforementioned relationships describe mean landslide thickness; added relationships relating landslide area to maximum landslide thickness are proposed. The presented results suggest limits on magnitudes of depth with landslide classification and relief between geologic settings. Consistent with prior research, it is observed that landslide area exhibits a first-order control on maximum and mean landslide thickness, but further insight is provided towards the influence of a limited set of landslide classification, which exhibit controls on the range of magnitudes observed for maximum landslide depth associated with failure. Lastly, the implications and limitations of the developed three-dimensional mapping approach is discussed.

This paper presents a linear applied element method (AEM) modeling approach for investigating the effects of the Mw 7.8 2015 Gorkha Nepal earthquake on a historical Pagoda temple. The impact of the earthquake is also investigated using linear FEM models and the results from both modeling approaches are compared. The numerical models are developed and calibrated using pre- and post-earthquake data collected from ambient vibration testing, lidar scans and data processing, and visual damage assessment. The numerical results indicate that the linear AEM models are as reliable as the finite element method (FEM) models to predict the modal response of the case study temple. In addition, the AEM models can potentially be further developed to estimate the damage from future earthquakes through nonlinear analyses. The present study not only informs the present health status of the masonry temple, but it also estimates how acceleration at the base of the temple is propagated through the height of the temple through comparison of floor response spectra, which could provide valuable insights into the force distribution over the height of the temple for the design of future retrofits.

Uncrewed aircraft systems (UASs) with integrated light detection and ranging (lidar) technology are becoming an increasingly popular and efficient remote sensing method for mapping. Due to its quick deployment and comparatively inexpensive cost, uncrewed laser scanning (ULS) can be a desirable solution to conduct topographic surveys for areas sized on the order of square kilometers compared to the more prevalent and mature method of airborne laser scanning (ALS) used to map larger areas. This paper rigorously assesses the accuracy and quality of a ULS system with comparisons to terrestrial laser scanning (TLS) data, total station (TS) measurements, and Global Navigation Satellite System (GNSS) check points. Both the TLS and TS technologies are ideal for this assessment due to their high accuracy and precision. Data for this analysis were collected over a period of two days to map a landslide complex in Mulino, Oregon. Results show that the digital elevation model (DEM) produced from the ULS had overall vertical accuracies of approximately 6 and 13 cm at 95% confidence when compared to the TS cross-sections for the road surface only and road and vegetated surfaces, respectively. When compared to the TLS data, overall biases of &minus, 2.4, 1.1, and &minus, 2.7 cm were observed in X, Y, and Z with a 3D RMS difference of 8.8 cm. Additional qualitative and quantitative assessments discussed in this paper show that ULS can provide highly accurate topographic data, which can be used for a wide variety of applications. However, further research could improve the overall accuracy and efficiency of the cloud-to-cloud swath adjustment and calibration processes for georeferencing the ULS point cloud.

Seismically-induced permanent displacements of slopes are commonly evaluated using the Newmark sliding block approach. The conventional Newmark approach, while convenient in application, is often applied to a singular potential failure mechanism, omits consideration of the spatial distribution of potential failure mechanisms, neglects complex yet realistic failure geometry, and does not consider the temporal evolution of the critical mechanism. The proposed diagnostic tool applies the Newmark approach within a rigorous limit equilibrium framework to produce spatial distributions of yield accelerations and seismically-induced permanent displacements, or yield maps and surface-associated displacement maps, respectively. In this study, the application and utility of these diagnostic tools are demonstrated through a sensitivity analysis considering various soil strength parameters, horizontal and vertical motions, and complex slope and failure geometries. Additionally, it is shown that the proposed diagnostic tool reasonably estimates post-seismic geometry, shown by a comparison to an experimental shake table study. Using yield and displacement maps, both coseismic displacement and the range of potentially unstable geometry may be constrained. Moreover, the spatial and temporal evolution of possible seismically-induced displacements may be considered. This tool provides a rational means of applying sliding block approaches that reduces the need to assume the surface of maximum displacement a priori while retaining the simplicity that has facilitated the application of the sliding block approach versus more complex numerical models. The proposed diagnostic tools also provide a framework for future potential analyses, as it can be modified to incorporate other slope stability methods or seismic analyses.