Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

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

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses.Comment: This paper was accepted to ISPRS Journal of Photogrammetry and Remote Sensing (https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing) on March 21, 2018. The official version will be made available on ScienceDirect (https://www.sciencedirect.com

Development of Bridge Information Model (BrIM) for digital twinning and management using TLS technology

In the current modern era of information and technology, the concept of Building Information Model (BIM), has made revolutionary changes in different aspects of engineering design, construction, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Model (BrIM), as a specific form of BIM, includes digital twining of the physical asset associated with geometrical inspections and non-geometrical data, which has eliminated the use of traditional paper-based documentation and hand-written reports, enabling professionals and managers to operate more efficiently and effectively. However, concerns remain about the quality of the acquired inspection data and utilizing BrIM information for remedial decisions in a reliable Bridge Management System (BMS) which are still reliant on the knowledge and experience of the involved inspectors, or asset manager, and are susceptible to a certain degree of subjectivity. Therefore, this research study aims not only to introduce the valuable benefits of Terrestrial Laser Scanning (TLS) as a precise, rapid, and qualitative inspection method, but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction using TLS-based point cloud, and to contribute to BrIM development. Moreover, this study presents a comprehensive methodology for incorporating generated BrIM in a redeveloped element-based condition assessment model while integrating a Decision Support System (DSS) to propose an innovative BMS. This methodology was further implemented in a designed software plugin and validated by a real case study on the Werrington Bridge, a cable-stayed bridge in New South Wales, Australia. The finding of this research confirms the reliability of the TLS-derived 3D model in terms of quality of acquired data and accuracy of the proposed novel slice-based method, as well as BrIM implementation, and integration of the proposed BMS into the developed BrIM. Furthermore, the results of this study showed that the proposed integrated model addresses the subjective nature of decision-making by conducting a risk assessment and utilising structured decision-making tools for priority ranking of remedial actions. The findings demonstrated acceptable agreement in utilizing the proposed BMS for priority ranking of structural elements that require more attention, as well as efficient optimisation of remedial actions to preserve bridge health and safety

Der Einsatz unbemannter Flugsysteme zur Charakterisierung von gesprengtem Haufwerk

Die erreichte Zerkleinerung und die Form des Haufwerks sind die beiden wichtigsten Ergebnisse einer Tagebausprengung. Schnelle Informationen über die Eigenschaften des gesprengten Haufwerks ermöglichen eine zielgerichtete und effiziente Produktionsplanung und Kenntnisse über die erreichte Zerkleinerung ermöglichen außerdem Anpassungen in der weiteren Zerkleinerungskette. Durch den Einsatz von UAVs (unmanned aerial vehicles) gemeinsam mit modernen Algorithmen aus dem Bereich Computer Vision und des maschinellen Lernens soll eine schnelle Erfassung und Interpretation der Daten bei gleichzeitiger Integration in die herkömmlichen betrieblichen Abläufe ermöglicht werden, und außerdem können Schwächen bodengebundener Systeme hinsichtlich Vollständigkeit und Repräsentativität umgangen werden. Im vorliegenden Beitrag wird einerseits auf den relevanten Stand des Wissens und der Technik eingegangen und andererseits wird die verfolgte Stoßrichtung bei der Systementwicklung dargelegt sowie erste Arbeiten präsentiert.The fragmentation and the shape of the muck pile are the two major outcomes of open pit mine and quarry blasts. Fast information about the muck pile properties will help to improve the production scheduling and furthermore this information could be used to optimize the blasting patterns of future production blasts. The combined use of unmanned aerial vehicles (UAVs) and modern machine learning and computer vision systems offers a new way of acquiring spatial data to determine on-site fragment size distribution, while at the same time enabling integration into common work flows and mitigating the weaknesses of ground-based systems with special regard to completeness and representativeness. In the present paper, we will discuss the relevant related work, present the planned path for system development and give examples of first work

UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd