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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]

Seacliff erosion dramatically alters the coastline of San Diego County through both marine and subaerial mechanisms, threatening development and public safety in the economically important coastal regions. Quantitative analysis of the seacliffs’ stability assists public policy makers to determine safe and effective coastline utilization. This dissertation develops concepts, techniques, and tools drawing from multiple disciplines to provide this essential information of both seasonal and rapid-event erosion.Terrestrial Laser Scanning (TLS) produces high resolution data to accurately quantify erosion and map hazard areas. Previous work applying TLS to seacliff erosion required additional, time-consuming surveying to georeference TLS surveys, limiting their application to only localized sites. This dissertation presents new methods to georeference TLS data for efficient, quantitative regional mapping and to quantitatively understand sources of uncertainty. The determination of optimal scanning parameters of spacing, distance, and sampling ratio ensures adequate data collection to capture the complex seacliff morphology. A new, automated algorithm for alignment shows how to constrain errors to avoid detrimental misalignment propagation, advancing TLS surveying for application to map long coastal sections. These techniques provide substantial time savings over previous methods, ensure consistent results between repeat surveys, and allow simultaneous study of both small and large scale geologic processes.The creation of a rapid-response program to observe sites immediately and continually after failure shows the insights TLS can provide in conjunction with wave, tidal, and climate data in understanding geologic processes governing seacliff erosion. The TLS data also showed that minimal wave energy is required to rework failure sediment and suggests that some failure masses can act as a stabilizing force for the cliffs by securing protective talus deposits against the cliff.The development of automated GIS routines to perform statistical change analysis is also presented. These routines were applied to both cliff and beach studies to show the importance of studying both small and large scale changes to understand geologic processes.These innovative methods provide a framework for future scientists and engineers to utilize TLS in understanding complex problems by ensuring sufficient data collection, establishing accurate, georeferenced baselines, and providing tools to perform meaningful change analysis.

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]