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The Americans with Disabilities Act (ADA) requires that the design, construction, and maintenance of curb ramps provide accessibility to persons with disabilities. Light detection and ranging (lidar) can efficiently collect high-accuracy and high-resolution three-dimensional (3D) geometric data, which can be leveraged for ADA compliance assessment in a virtual environment to help guide and improve field practice. Most existing studies do not consider many aspects of the field inspection process including local variations in slope measurements and the physical instrument characteristics, warranting a more detailed accuracy evaluation. Accordingly, a workflow for virtual ADA compliance inspection of curb ramps using lidar data is proposed and was applied to a controlled experiment using full-size ramp specimens to optimize parameters and compare methods with rigorous accuracy assessments. The proposed workflow consists of data preprocessing, digital elevation model generation, and digital inclinometer simulation. In the digital inclinometer simulation, we developed and implemented four different approaches including surface normal, immediate neighbor linear regression, scaled neighbor linear regression, and touching point extraction. Among these approaches, the novel touching point extraction method considers both the roughness of the surface and the scale in which a digital inclinometer measures the slope. The accuracy and effectiveness of the framework were demonstrated, resulting in a root-mean square error of 0.18% for the mean slope, which is on par with the accuracy of a digital inclinometer (0.2%, 1−σ). An example virtual experiment is provided using the proposed approach to identify the optimal spacing of measurement samples to determine the slope of a curb ramp for ADA compliance assessment. [ABSTRACT FROM AUTHOR]

Regional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA. Plain Language Summary: Landslides are potentially destructive natural hazards that may impact topography, ecology, and important infrastructure. Often, previously triggered landslides are used to better understand where landslides may occur in the future, but in some regions, previous landslides may be poorly documented or characterized. In lieu of these data, models capturing the physics tied to landsliding may be paired with digital topography to predict landslide potential across landscapes. However, existing models are limited in their ability to efficiently capture the realistic geometry of three‐dimensional (3D) landslides across large landscapes. This study presents RegionGrow3D, a new model that identifies slope instabilities throughout large regions, and then grows those instabilities into landslides of previously unknown geometry by balancing forces within the underlying soil. The model is applied to a region in the Oregon Coast Range, USA to develop empirical relationships between landslide area, volume, and frequency for a range of rainfall scenarios and to determine the amount of rainfall required to trigger landslides on specific terrain features. Furthermore, because soil parameters are highly uncertain at this spatial scale (≥1,000 km2), a 3D landslide model is used to forensically assess soil strength from previously documented landslides in the area. Key Points: RegionGrow3D is a regional‐scale three‐dimensional slope stability model that grows discrete landslide volumes to achieve force equilibriumRegionGrow3D is parameterized using a range of input parameters to quantify the size, location, and likelihood of landslide initiationModeled landslides are used to quantify geomorphic scaling laws and rainfall thresholds that consider model input uncertainty [ABSTRACT FROM AUTHOR]

Background: The expanding use of Uncrewed Aircraft System (UAS) technology in disaster response shows its immense potential to enhance emergency management. However, there is limited documentation on the challenges and data management procedures related to UAS operation. Aims: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Methods: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Key results: UAS technology plays a significant role in search and rescue, reconnaissance, mapping, and damage assessment, alongside notable challenges such as extreme flying conditions, data processing difficulties, and airspace authorisation complexities. Conclusions: The study concludes with the need for updated infrastructure standards, streamlined policies, and better coordination between technological advancements and political processes, emphasising the necessity for reform to enhance disaster response capabilities. Implications: The findings of this study inform future guidelines for the effective and safe use of UAS in disaster situations, advocating for a bridge between state-of-the-art UAS research and its practical application in emergency response. We document how Uncrewed Aircraft System (UAS) technology was used in the 2020 Labour Day wildfires in Oregon, USA. Information from a literature review, social media, and interviews with disaster responders were compiled. Qalitative analyses on the interview data synthesised typical UAS applications and highlighted challenges. [ABSTRACT FROM AUTHOR]

Summarized in this article are the major findings from a detailed damage investigation of an instrumented ductile reinforced concrete moment frame building subjected to a design-level earthquake. The building sustained widespread damage during the 2016 M-7.8 Kaikoura earthquake and was subsequently demolished. Prior to demolition, the structural system was extensively surveyed to document the severity and distribution of the damage sustained in the reinforced concrete frames and the flooring system. Furthermore, the building response during the earthquake was reconstructed from the acceleration records obtained from instruments installed throughout the building. The site seismic demands were also obtained from a local free-field instrument. The data collected from this damage investigation provide a high-quality dataset that is valuable to researchers investigating different parameters related to the seismic performance of reinforced concrete moment frame buildings. The dataset is published and publicly available on DesignSafe-CI (project PRJ-3444); https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3444v2. The data collection methodology is described, and a roadmap for navigating the dataset is presented to support future use of the archived dataset. [ABSTRACT FROM AUTHOR]

Road markings are essential features to convey important information to various roadway users such as pedestrians, bicyclists, and motorists. Although mobile laser scanning (MLS) technology provides dense and spatially accurate data, automated identification of road markings remains a challenge. Although approaches have been developed to detect road markings from point clouds, prior studies have not thoroughly investigated road discretization and road marking clustering, which are essential for effective instance-level identification and management of road markings. To improve road discretization, our approach uses line segmentation to divide the trajectory of MLS data and fit circles to individual segments. By calculating the intersection between the road points and the circle's center for each trajectory segment, this method ensures no overlap occurs when discretizing roads with sharp turns. To improve road marking clustering, the discretized points are rasterized onto a two-dimensional (2D) image and clustered using connected component labeling. Individual markings are skeletonized to detect junctions and corners, which enables the separation of underclustered road markings. Overclustered road markings are then merged using a rule-based approach optimized with a traffic line manual. The proposed approach was evaluated through extensive experiments using 28 MLS data sets containing 2,340 road marking clusters with complex geometry and wear. Out of these, only 64 instances (2.7%) were falsely clustered, achieving a precision rate of 97.5% and a recall rate of 99.7%. Practical Applications: Efficient road maintenance and improved road safety rely heavily on the clear visibility of road markings, which guide diverse users, such as pedestrians, cyclists, and drivers. Traditional methods for inspecting these markings are often time-consuming and prone to human error. In this study, we use MLS technology to collect comprehensive and precise data on roadways. We have developed an automated technique that significantly enhances the identification and management of road markings. This method accurately rasterizes and segments roadway lidar data—even in areas with sharp turns—to apply advanced image processing for the recognition of individual markings, including those that are substantially worn or have complex geometries. This simplifies the geometric data in a GIS database such that each line object is more representative of how someone would manually digitize the marking. Tested on numerous data sets, our approach achieved an accuracy rate of over 97% in clustering individual road markings. The developed approach not only streamlines roadway maintenance but also offers potential benefits for intelligent transportation systems. It contributes to safer autonomous vehicle navigation and supports informed decision-making regarding road infrastructure, ultimately improving all road users' experience. [ABSTRACT FROM AUTHOR]

Dense three-dimensional (3D) point clouds collected from rapidly evolving data acquisition techniques such as light detection and ranging (lidar) and structure from motion (SfM) multiview stereo (MVS) photogrammetry contain detailed geometric information of a scene suitable for a wide variety of applications. Among the many processes within a typical point cloud processing workflow, segmentation is often a crucial step to group points with similar attributes to support more advanced modeling and analysis. Segmenting large point cloud data sets (i.e., hundreds of millions to billions of points) can be extremely time consuming and tedious with current tools, which primarily rely on significant manual effort. While many automated methods have been proposed, the practicality, scalability, and versatility of these approaches remain a bottleneck stifling processing of large data sets. To overcome these challenges, this paper introduces a novel, generalized segmentation framework called Vo-Norvana, which incorporates a new voxelization technique, a normal variation analysis considering the positioning uncertainty of the point cloud, and a custom region growing process for clustering. The proposed framework was tested with several large-volume data sets collected in diverse scene types using several data acquisition platforms including terrestrial lidar, mobile lidar, airborne lidar, and drone-based SfM-MVS photogrammetry. In evaluating the accuracy of models generated from Vo-Norvana against manual segmentation, the average error of the position, orientation, and dimensions are 2.7 mm, 0.083°, and 0.9 mm, respectively. Over 0.2 million points per second and 36 thousand voxels per second can be achieved when segmenting an airborne lidar data set containing over 639 million points to about 1 million segments. [ABSTRACT FROM AUTHOR]

Important infrastructure such as highways or railways traverse unstable terrain in many mountainous and scenic parts of the world. Rockfalls and landslides result in frequent maintenance needs, system unreliability due to frequent closures and restrictions, and safety hazards. Seismic activity significantly amplifies these negative economic and community impacts by generating large rockfalls and landslides as well as weakening the terrain. This paper interrogates a rich database of repeat terrestrial lidar scans collected during the Canterbury New Zealand Earthquake Sequence to document geomorphic processes as well as quantify rockfall activity rates through time. Changes in the activity rate (spatial distribution) and failure depths (size) were observed based on the Rockfall Activity Index (RAI) morphological classification. Forecasting models can be developed from these relationships that can be utilized by transportation agencies to estimate increased maintenance needs for debris removal to minimize road closures from rockfalls after seismic events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Surveying educators’ conferences in the USA and Canada started in 1937 at Rainy Lake, Minnesota, which was hosted by Iowa State. The educators’ main concerns at that time were focused on surveying instruction in civil engineering programs and the lack of a national presence for the surveying profession. Since that time, 28 conferences have taken place, continuing the “spirit of Rainy Lake.” The first conferences were actively supported by American Society for Engineering Education (ASEE) until the 1970s, when organization of the conferences was then passed to the hosting institution. Challenges facing surveying education in the past 20 years prompted the surveying educators to form a new society in 2009, the Surveying and Geomatics Educators Society (SaGES). Since then, SaGES has organized the conference supporting surveying educators and surveying education in general. This paper documents the history of those conferences and the events that led to the formation of SaGES. By revisiting each conference since 1937, we can understand the challenges and issues surrounding surveying education in each period as well as how surveying education evolved between 1937 and 2021. This paper explores the historical journey of the development and evolution of surveying education in the USA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 and time compared with many other techniques. Given the large volume of data within a mobile LiDAR point cloud, automation of processing and analysis is critical to improve the efficiency of the entire workflow, particularly for common tasks of ground filtering (separating points representing the ground from non-ground objects) and road detection (identifying and extracting the road surface). This paper proposes a novel and highly efficient method of segment-based ground filtering and adaptive road detection from mobile LiDAR data. The proposed method includes four principal steps: (1) preprocessing of the mobile LiDAR point cloud with data merging and splitting, (2) an improved Mo-norvana trajectory reconstruction and segmentation, (3) segment-based ground filtering via a segment analysis followed by a scanline analysis, and (4) road detection including an adaptive rasterization and vehicle access analysis. The proposed method is demonstrated to be robust, effective, and efficient by testing on representative datasets collected with different speeds in a rural/highway and an urban/suburban scene. The performance of our method is further evaluated quantitatively through a model-based accuracy assessment by comparison to a model generated from manually extracted ground points where the F1 score and Root Mean Square Error of the elevation model are 98.14% and 0.0027 m, and 99.16% and 0.0004 m for the rural and suburban datasets, respectively. [ABSTRACT FROM AUTHOR]

Photogrammetry using structure from motion (SfM) and multiview stereopsis (MVS) techniques can recover three-dimensional (3D) structure from a set of overlapping, unoriented, and uncalibrated images captured by nonmetric digital cameras. It is possible to generate accurate reconstructions of sparse points using mathematically robust bundle adjustment procedures together with accurate surveying control data. However, MVS, which recovers the dense geometry by matching and expanding between sparse points, is prone to additional error. Miscellaneous constituents such as sensor specifications, data collection, and site conditions can introduce random noise or artifacts that locally degrade the accuracy of the dense point cloud. This paper proposes seven indexes, named dense point cloud quality factors (DPQFs), as proxy indicators of image-based dense reconstruction accuracy. DPQFs include proximity to keypoint features, distance to GCPs, angle of incidence, camera stand-off distances, number of overlapping images, brightness index, and darkness index. The correlation between the DPQFs and the 3D error was investigated in simulated and empirical experiments scenarios with varying factors. The results of this study showed that the DPQFs provide proxy indications for accuracy when the error estimation for the dense point clouds is more challenging than error propagation computations in bundle adjustment (BA). The DPQFs can be defined solely using the SfM-MVS data, without prior knowledge about the error. Inclusion of the factors as additional fields of information and their visualization provide tangible intuitions regarding the factors that influence the accuracy of image-based 3D reconstruction. [ABSTRACT FROM AUTHOR]

Landslides occur in a variety of forms that are a function of climactic setting, tectonic setting, geomorphic and geologic setting, and the shear strength of soil and rock. While major advances in characterizing the spatial influence of these settings on landslide activity have occurred in recent years, there has been limited progress on understanding spatial trends in shear strength properties and their influence on landslide activity. Herein, we propose a regional‐scale forensic methodology to establish first‐order estimates of landslide shear strength. This approach is performed using (1) interpolation of rupture surface geometry and (2) three‐dimensional slope stability analysis. Thereafter, distributions of back‐analyzed shear strengths of landslide inventories are used to demonstrate that distinctly different trends are observed when comparing geologic units and landslide type. It is shown that there is generally an inverse correlation between landslide volume and friction angle, suggesting that larger earthflows tend to be in a residual state of shear, while smaller deep‐seated failures tend to occur in stronger, possibly cemented materials. Significant uncertainty lies in characterization of hydrological conditions; nonetheless, upper and lower bounds of groundwater conditions still reflect significant differences in inferred regional trends in shear strength properties between geologic settings. A comparison of three‐dimensional forensic back analyses to conventional, infinite slope conditions often used in susceptibility analyses suggests that conventional approaches may significantly overestimate landslide shear strengths at a regional scale. Key Points: Landslide inventories are used to characterize trends in shear strengths at a regional scale through three‐dimensional stability analysesThere is an inverse correlation between landslide volume and friction angle, potentially reflecting landslide mechanismThree‐dimensional forensic analyses suggest that infinite slope stability analyses overestimate shear strengths at a regional scale [ABSTRACT FROM AUTHOR]

Pavement markings are produced with retroreflective materials to enhance visibility for motorists, particularly at night. Retroreflectivity evaluation throughout an extensive highway network for maintenance and asset management purposes is a critical, yet challenging task for transportation agencies because visual evaluation can often be subjective and inconsistent, while field measurement can be time-consuming. Mobile Light Detection and Ranging (Lidar) datasets can potentially provide a safe, cost-effective, and reliable method of performing the required evaluation. This paper presents an empirical model for radiometric calibration of Lidar intensity information from the Leica Pegasus:Two system for pavement marking evaluation. The model was developed using dense handheld retroreflectometer measurements and mobile Lidar data collected in a variety of geometric configurations on a test site consisting of various markings with varying degrees of wear. The quantitative accuracy assessment of the proposed radiometric calibration model for estimating retroreflectivity was conducted to another independent dataset collected in different lanes and system configurations. [ABSTRACT FROM AUTHOR]

Development of a digital elevation model (DEM) with terrestrial laser scanning (TLS) point cloud data of inadequate resolution and/or completeness will result in a poor-quality DEM with significant gaps. In many cases, this fact can be obscured from the user because spatial interpolation (e.g., generating a surface mesh or kriging) is often performed prior to or during DEM creation, resulting in a seamless DEM in which data gaps in the source TLS data have been filled in. However, evaluating the completeness of DEMs created directly from the point cloud data provides more feedback regarding the suitability of a given TLS point cloud for supporting a specified DEM resolution. An empirical DEM completeness database and associated TLS acquisition planning tool is proposed that can assist in optimizing TLS survey parameters (quantity of scans and scanning resolution) for achieving a high-quality DEM of specified resolution and completeness. [ABSTRACT FROM AUTHOR]

Many landslides cause destruction through progressive, gradual movements that stem from precipitation and toe erosion caused by marine or riverine processes. The magnitude and timing of seasonal landslide movement may be influenced by changing slide mass geometry and pore pressure response. The rate of landslide advance and potential for sustained movement has previously been studied in the context of hydrological factors, such as dilative or contractive pore pressure response, but the sensitivity of realistic landslide geometry subject to toe erosion is not well quantified. The purpose of this study is to assess the sensitivity of landslide movements to seasonal pore pressure increases and erosive processes. This assessment is performed through the development of a limit equilibrium framework couples time‐dependent feedback between coulomb friction, pore pressure, and evolving landslide geometry. This enables quantification of the relative influence of time‐dependent changes in pore pressure and rates of toe undercutting on landslide advance. The proposed model shows good agreement with monitoring data from three landslides in coastal Oregon, USA. Sensitivity to erosion, pore pressure, and geometry are assessed. Although fluctuations in pore pressures are found to be the primary driver for landslide movements at the sites, there is significant, nonlinear sensitivity to plausible erosion rates. This sensitivity is particularly apparent for landslides that tend to be short in length as the removal of buttressing materials is proportionally more destabilizing. These findings are an important consideration, particularly as increasingly extreme storm events that will likely magnify landslide movements in coastal or fluvial environments. Key Points: A model is presented that captures the coupled relationship between undercutting, pore pressure change, and progressive landslide movementThe evolution of landslide geometry, undercutting rates, and pore pressure fluctuations influence the time‐dependent movements that are observedSmaller, progressive landslides demonstrate particular sensitivity to undercutting, highlighting the potential impacts of increased wave attack or fluvial erosion [ABSTRACT FROM AUTHOR]

The authors discuss a report on the lateral spreading behaviour of slope comprising a column of sliding blocks with degrading yield accelerations using the modified Newmark's sliding block concept.

The Surveying and Geomatics Educators Society (SaGES) held their 26th Biannual Conference at Oregon State University (OSU) in Corvallis, OR, from July 30 to August 3, 2017. This innovative conference provides an opportunity to discuss and find solutions to the many challenges and opportunities facing geomatics education. It also enables educators to develop and share effective teaching strategies for the variety of content needed by geomatics students. The conference theme, "Where the Point Meets the Cloud," focused on finding strength in numbers by working together to tackle the many challenges facing the geomatics profession. [ABSTRACT FROM AUTHOR]

Abstract: This study examines three sites in Alaska with unstable rock slopes that were surveyed using both terrestrial laser scanning (TLS) and structure‐from‐motion (SfM) techniques. The datasets were acquired simultaneously and linked to a rigorous survey control network. An accuracy evaluation of the SfM‐derived surface models was performed using the TLS data and numerous reflectorless total station observations collected across the rock slopes. A quality evaluation was conducted to examine differences in point density, model completeness and distributions of morphological properties between the SfM and TLS datasets. The results indicate that SfM is a viable option for unstable rock‐slope assessment when a sufficient number of images with adequate overlap are acquired and the reconstruction is tied to a survey control network. The best results, in terms of accuracy and completeness, were achieved when combining both unmanned aerial vehicle (UAV) and terrestrial imagery for the SfM reconstruction. However, issues such as over‐smoothing and geometric inconsistencies bring into question the suitability of SfM for the detection of small changes over time. [ABSTRACT FROM AUTHOR]