Landslide monitoring using mobile device and cloud-based photogrammetry

PhD ThesisLandslides are one of the most commonly occurring natural disasters that can cause a serious threat to human life and society, in addition to significant economic loss. Investigation and monitoring of landslides are important tasks in geotechnical engineering in order to mitigate the hazards created by such phenomena. However, current geomatics approaches used for precise landslide monitoring are largely inappropriate for initial assessment by an engineer over small areas due to the labourintensive and costly methods often adopted. Therefore, the development of a costeffective landslide monitoring system for real-time on-site investigation is essential to aid initial geotechnical interpretation and assessment. In this research, close-range photogrammetric techniques using imagery from a mobile device camera (e.g. a modern smartphone) were investigated as a low-cost, non-contact monitoring approach to on-site landslide investigation. The developed system was implemented on a mobile platform with cloud computing technology to enable the potential for real-time processing. The system comprised the front-end service of a mobile application controlled by the operator and a back-end service employed for photogrammetric measurement and landslide monitoring analysis. In terms of the backend service, Structure-from-Motion (SfM) photogrammetry was implemented to provide fully-automated processing to offer user-friendliness to non-experts. This was integrated with developed functions that were used to enhance the processing performance and deliver appropriate photogrammetric results for assessing landslide deformations. In order to implement this system with a real-time response, the cloud-based system required data transfer using Internet services via a modern 4G/5G network. Furthermore, the relationship between the number of images and image size was investigated to optimize data processing. The potential of the developed system for monitoring landslides was investigated at two different real-world UK sites, comprising a natural earth-flow landslide and coastal cliff erosion. These investigations demonstrated that the cloud-based photogrammetric measurement system was capable of providing three-dimensional results to subdecimeter-level accuracy. The results of the initial assessments for on-site investigation could be effectively presented on the mobile device through visualisation and/or statistical quantification of the landslide changes at a local-scale.Royal Thai Government and Naresuan University for the scholarship and financial suppor

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

John F. Kennedy Space Center's Technology Development and Application 2006-2007 Report

Topics covered include: Reversible Chemochromic Hydrogen Detectors; Determining Trajectory of Triboelectrically Charged Particles, Using Discrete Element Modeling; Using Indium Tin Oxide To Mitigate Dust on Viewing Ports; High-Performance Polyimide Powder Coatings; Controlled-Release Microcapsules for Smart Coatings for Corrosion Applications; Aerocoat 7 Replacement Coatings; Photocatalytic Coatings for Exploration and Spaceport Design; New Materials for the Repair of Polyimide Electrical Wire Insulation; Commodity-Free Calibration; Novel Ice Mitigation Methods; Crack Offset Measurement With the Projected Laser Target Device; New Materials for Structural Composites and Protective Coatings; Fire Chemistry Testing of Spray-On Foam Insulation (SOFI); Using Aerogel-Based Insulation Material To Prevent Foam Loss on the Liquid-Hydrogen Intertank; Particle Ejection and Levitation Technology (PELT); Electrostatic Characterization of Lunar Dust; Numerical Analysis of Rocket Exhaust Cratering; RESOLVE Projects: Lunar Water Resource Demonstration and Regolith Volatile Characterization; Tribocharging Lunar Soil for Electrostatic Beneficiation; Numerically Modeling the Erosion of Lunar Soil by Rocket Exhaust Plumes; Trajectory Model of Lunar Dust Particles; Using Lunar Module Shadows To Scale the Effects of Rocket Exhaust Plumes; Predicting the Acoustic Environment Induced by the Launch of the Ares I Vehicle; Measuring Ultrasonic Acoustic Velocity in a Thin Sheet of Graphite Epoxy Composite; Hail Size Distribution Mapping; Launch Pad 39 Hail Monitor Array System; Autonomous Flight Safety System - Phase III; The Photogrammetry Cube; Bird Vision System; Automating Range Surveillance Through Radio Interferometry and Field Strength Mapping Techniques; Next-Generation Telemetry Workstation; GPS Metric Tracking Unit; and Space-Based Range

USING UNMANNED AERIAL SYSTEMS (UAS) AND PHOTOGRAMMETRY TO REMOTELY ASSESS LANDSLIDE EVENTS IN NEAR REAL-TIME

Commercially available unmanned aerial systems (UAS) and photogrammetry software have undergone rapid advancements in recent years. However, the use of UAS and photogrammetry techniques for monitoring surface landform deformation has not been adopted for the most part due to complicated workflows and complex UAS systems. This study demonstrates the ability to monitor landslides in near-real time with commercially available UAS and photogrammetry software using direct georeferencing and co- registration techniques. The results of this research were then assessed to develop an optimal workflow for the rapid assessment of surface deformations with direct georeferenced UAS obtained imagery and photogrammetry software

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees\u27 DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were -8.55-14.88 cm and -12.07-24.49 cm, respectively, and the corresponding RMSEs were 12.94-33.96 cm and 17.78-28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Understanding the variability in vehicle dynamics and emissions at urban obstacles

Roadworks are a feature of the road network that can cause vehicles to deviate from their desired speed or trajectory. This may negatively impact traditional measures of network performance such as travel time, or result in changes to tailpipe emission rates. The impact of roadworks on tailpipe emission rates is of interest due to the harmful pollutants that are released during the combustion process. Pollutants such as nitrogen oxides (NOx) are toxic to humans, and carbon dioxide (CO2) is a greenhouse believed to influence human-induced global climate change. In order to investigate methods of reducing the environmental impact of roadworks and other obstacles in the road network, modelling tools may be used. However, it is essential that the tools are appropriate for modelling these features of the road network. In order to assess the suitability of existing traffic and emission modelling tools, an understanding of the variability in vehicle dynamics and emissions at urban obstacles is first required. In this thesis, a dataset that contains real-world tailpipe emissions and vehicle dynamics data, from vehicles in the vicinity of urban obstacles such as roadworks, is assembled. This is achieved using a portable emission measurement system (PEMS) and a high-resolution trajectory monitoring platform developed as part of this research. Through analysis of the acceleration behaviour and tailpipe emission rates at different urban obstacles and from different vehicles, an understanding of the variability is formed. The findings from the analysis of behaviours observed in the vicinity of urban obstacles are then used to adapt existing traffic and emissions modelling tools. The error between measured and modelled emissions is shown to reduce from over 30% to under 12% for CO2 emissions. Based on the findings of a roadworks case study, recommendations are made to policy makers and the modelling community.Open Acces

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments