Unmanned Aircraft System Assessments of Landslide Safety for Transportation Corridors

An assessment of unmanned aircraft systems (UAS) concluded that current, off-the-shelf UAS aircraft and cameras can be effective for creating the digital surface models used to evaluate rock-slope stability and landslide risk along transportation corridors. The imagery collected with UAS can be processed using a photogrammetry technique called Structure-from-Motion (SfM) which generates a point cloud and surface model, similar to terrestrial laser scanning (TLS). We treated the TLS data as our control, or “truth,” because it is a mature and well-proven technology. The comparisons of the TLS surfaces and the SFM surfaces were impressive – if not comparable is many cases. Thus, the SfM surface models would be suitable for deriving slope morphology to generate rockfall activity indices (RAI) for landslide assessment provided the slopes. This research also revealed that UAS are a safer alternative to the deployment and operation of TLS operating on a road shoulder because UAS can be launched and recovered from a remote location and capable of imaging without flying directly over the road. However both the UAS and TLS approaches still require traditional survey control and photo targets to accurately geo-reference their respective DSM.List of Figures ...................................................................................................... vi List of Abbreviations ......................................................................................... vii Acknowledgments ................................................................................................ x Executive Summary ............................................................................................. xi CHAPTER 1 INTRODUCTION .......................................................................... 1 CHAPTER 2 LITERATURE REVIEW ................................................................ 4 2.1 Landslide Hazards .................................................................................... 4 2.2 Unmanned Aircraft Systems Remote Sensing.......................................... 6 2.3 Structure From Motion (SfM) .................................................................. 7 2.4 Lidar terrain mapping ............................................................................... 8 CHAPTER 3 STUDY SITE/DATA .................................................................. 11 CHAPTER 4 METHODS ................................................................................ 13 4.1 Data Collection ............................................................................................. 13 4.1.1 Survey Control ..................................................................................... 14 4.1.2 TLS Surveys ........................................................................................ 16 4.1.3 UAS Imagery ....................................................................................... 17 4.1.4 Terrestrial Imagery Acquisition ........................................................... 19 4.2 Data Processing ............................................................................................ 20 4.2.1 Survey Control ..................................................................................... 20 4.2.2 TLS Processing .................................................................................... 20 4.2.3 SfM Processing .................................................................................... 21 4.2.4 Surface Generation .............................................................................. 22 4.3 Quality Evaluation ........................................................................................ 23 4.3.1 Completeness ....................................................................................... 23 4.3.2 Data Density/Resolution ...................................................................... 23 4.3.3 Accuracy Assessment .......................................................................... 23 4.3.2 Surface Morphology Analysis ............................................................. 24 4.2.6 Data Visualization ............................................................................... 25 CHAPTER 5 RESULTS ................................................................................. 27 v 5.1 UTIC DSM evaluation.................................................................................. 27 5.1.1 Completeness evaluation ..................................................................... 28 5.1.2 Data Density Evaluation ...................................................................... 29 5.1.3 Accuracy Evaluation............................................................................ 30 5.2 Geomorphological Evaluation ...................................................................... 32 CHAPTER 6 DISCUSSION ............................................................................ 35 6.1 Evaluation of UAS efficiencies .................................................................... 35 6.2 DSM quality and completeness .................................................................... 37 6.3 Safety and operational considerations .......................................................... 37 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS ................................ 40 7.1 Technology Transfer..................................................................................... 41 7.1.1 Publications ......................................................................................... 41 7.1.2 Presentations ........................................................................................ 42 7.1.3 Multi-media outreach .......................................................................... 43 6.4 Integration of UAS and TLS data ................................................................. 44 REFERENCES .............................................................................................. 4

Massively Scalable Inverse Reinforcement Learning in Google Maps

Optimizing for humans' latent preferences is a grand challenge in route recommendation, where globally-scalable solutions remain an open problem. Although past work created increasingly general solutions for the application of inverse reinforcement learning (IRL), these have not been successfully scaled to world-sized MDPs, large datasets, and highly parameterized models; respectively hundreds of millions of states, trajectories, and parameters. In this work, we surpass previous limitations through a series of advancements focused on graph compression, parallelization, and problem initialization based on dominant eigenvectors. We introduce Receding Horizon Inverse Planning (RHIP), which generalizes existing work and enables control of key performance trade-offs via its planning horizon. Our policy achieves a 16-24% improvement in global route quality, and, to our knowledge, represents the largest instance of IRL in a real-world setting to date. Our results show critical benefits to more sustainable modes of transportation (e.g. two-wheelers), where factors beyond journey time (e.g. route safety) play a substantial role. We conclude with ablations of key components, negative results on state-of-the-art eigenvalue solvers, and identify future opportunities to improve scalability via IRL-specific batching strategies

To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection

Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitude-frequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.Keywords: change detection, surface modeling, point cloud, geohazards, laser scanning, lidar, rockfallsThis is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by MDPI. The published article can be found at: http://www.mdpi.com/journal/remotesensin

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality

Transportation Corridor Resiliency in the Face of a Changing Climate

The effects of a changing climate on transportation corridor slopes are poorly understood, but several recent studies have suggested that landslide activity, especially rockfall, is likely to increase as a consequence of the increased occurrence of intense precipitation events. Effects from climate change such as extreme temperature fluctuations, freeze-thaw cycles, and increased rainfall quantity and intensity weaken geologic materials, exacerbating slope failures. In order to understand slope rockfall activity and its linkages to weather and climate, we acquired additional high-resolution lidar data and unmanned aircraft systems structure data from motion surveys of rock slopes in Alaska. Over several projects we have successively developed a rich data set spanning 5 years to quantitatively evaluate rockfall activity (the magnitude-frequency of rockfall events), which proved useful for examining correlations with historic weather patterns and future climate forecasts. As part of this research, we further developed the Rockfall Activity Index (RAI) and began to evaluate how the RAI can be linked to increasing temperature swings and freeze-thaw cycles. This quantitative approach for rockfall activity forecasting is an important step in providing tools to state departments of transportation to assess transportation corridor risks, sustainability, and resiliency, especially for Alaska in the face of a changing climate. This research is a first step in providing the analysis tools needed to meet a recent presidential directive and help improve our fundamental understanding of the potential impacts of climate change on the safety of and mobility within transportation networks in landslide-prone regions such as the Pacific Northwest in the U.S.Pacific Northwest Transportation Consortium Alaska Department of Transportation & Public Facilitie

Global impact of the first coronavirus disease 2019 (COVID-19) pandemic wave on vascular services

This online structured survey has demonstrated the global impact of the COVID-19 pandemic on vascular services. The majority of centres have documented marked reductions in operating and services provided to vascular patients. In the months during recovery from the resource restrictions imposed during the pandemic peaks, there will be a significant vascular disease burden awaiting surgeons. One of the most affected specialtie