Inferring the most probable maps of underground utilities using Bayesian mapping model

Bilal, Muhammad; Khan, Wasiq; Muggleton, J.M.; Rustighi, Emiliano; Jenks, Hugo; Pennock, S.R.; Atkins, P.R.; Cohn, Anthony G.

doi:10.1016/j.jappgeo.2018.01.006

Cited by 18 publications

(13 citation statements)

References 22 publications

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“…Also, the utilization of these numerical models is currently limited by the availability of hydrologic process studies and observational data to support their parameterization, calibration and validation. In addition, in many cities, accurate data on as-built subterranean drainage infrastructure is currently unavailable, presenting opportunities for both the development of novel sensing techniques and statistical methods for probabilistic mapping of sewer networks (Bilal et al, 2018;Hopkins & Bain, 2018;S ar acin, 2017). 3.…”

Section: Pluvial Flooding Research Opportunitiesmentioning

confidence: 99%

Pluvial flood risk and opportunities for resilience

et al. 2018

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The risk presented by pluvial flooding has emerged as a critical issue in urban water management. Pluvial flooding occurs when precipitation intensity exceeds the capacity of natural and engineered drainage systems, and it is expected to increase in frequency, severity and impact through the 21st century due to the combined effects of climate change and urbanization. Although there have been recent advances in approaches to assess the risk presented pluvial flooding and to enhance the resilience of cities to its impacts, they have not been broadly implemented and there are many opportunities for additional research. We provide case studies of pluvial flooding in six cities in the continental United States, which serve as examples of the current vulnerability of cities that have not developed comprehensive pluvial flood management plans and the challenges in conducting pluvial flood research in light of existing data gaps. We also identify key research challenges that should be prioritized by the interdisciplinary water research community to better support urban resilience practice. This article is categorized under: Engineering Water > Sustainable Engineering of Water Science of Water > Water Quality Engineering Water > Planning Water

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Section: Pluvial Flooding Research Opportunitiesmentioning

confidence: 99%

Pluvial flood risk and opportunities for resilience

et al. 2018

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“…As is seen from Table 2 some studies were focused on determining the positional and height accuracy of UUI in a test environment [5,18,19,27] and a few in a real urban site [23][24][25][26]. None of them, other than Šarlah et al [5] and Gabryś et al [27], use TPS to determine the position of the GPR antenna in kinematic mode.…”

Section: Introductionmentioning

confidence: 99%

“…None of them, other than Šarlah et al [5] and Gabryś et al [27], use TPS to determine the position of the GPR antenna in kinematic mode. In Dou et al [24] and Bilal et al [23], a unique marching cross-section algorithm and Bayesian mapping model with implementing various machine-learning techniques for automatically locating UUI segments by fusing data from multiple sensors are introduced. All profiles measured with a GPR and other sensors were later calibrated on a known previously established coordinate system determinate with TPS.…”

Section: Introductionmentioning

confidence: 99%

“…By using GPR data, 60.7% of the UUI in the urban site were detected closer than 30 cm according to the assumed true position and heights. In [23] the positional accuracy given with the mean spatial error S(x;y) is 163 cm for urban site I and 100 cm for urban site II. S(x;y) represents the mean spatial distance from the true to the estimated position.…”

Section: Introductionmentioning

confidence: 99%

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Application of Kinematic GPR-TPS Model with High 3D Georeference Accuracy for Underground Utility Infrastructure Mapping: A Case Study from Urban Sites in Celje, Slovenia

et al. 2020

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This paper describes in detail the applicability of the developed ground-penetrating radar (GPR) model with a kinematic GPR and self-tracking (robotic) terrestrial positioning system (TPS) surveying setup (GPR-TPS model) for the acquisition, processing and visualisation of underground utility infrastructure (UUI) in a real urban environment. The integration of GPR with TPS can significantly improve the accuracy of UUI positioning in a real urban environment by means of efficient control of GPR trajectories. Two areas in the urban part of Celje in Slovenia were chosen. The accuracy of the kinematic GPR-TPS model was analysed by comparing the three-dimensional (3D) position of UUI given as reference values (true 3D position) from the officially consolidated cadastre of utility infrastructure in the Republic of Slovenia and those obtained by the GPR-TPS method. To determine the reference 3D position of the GPR antenna and UUI, the same positional and height geodetic network was used. Small unmanned aerial vehicles (UAV) were used for recording to provide a better spatial display of the results of UUI obtained with the GPR-TPS method. As demonstrated by the results, the kinematic GPR-TPS model for data acquisition can achieve an accuracy of fewer than 15 centimetres in a real urban environment.

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“…In 2016, the societal damage caused by utility strikes in the US came to an estimated $1.5 billion [4]. The main causes of this problem were the lack of data on urban underground spaces (UUS), and miscommunications between utility owners and contractors [5,6].…”

Section: Introductionmentioning

confidence: 99%

Integrating Geographic Information Systems and Augmented Reality for Mapping Underground Utilities

et al. 2019

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Underground infrastructure is a critical component of the basic utility services provided to society. The single largest threat to the safety of underground utility lines is being struck by construction earthwork projects. One of the causes of this problem is miscommunication between utility owners and contractors. Therefore, it is vitally important to coordinate resources, share information, and ensure efficient communication between construction personnel and utility owners. Geographic information systems (GIS) provide a solution for interoperability in the construction industry. Applying such technologies in the field of underground construction requires accurate and up-to-date information. Augmented reality (AR) has been identified as a technique that could enhance information extraction from the virtual world to the real world and improve the access and utilization of information. However, there is currently limited research that has integrated AR and GIS and evaluated the effectiveness and usability of the combination in this domain. The main objective of this research was to develop an integrated AR-GIS for mapping and capturing underground utilities using a mobile device. The data are shared instantaneously with other stakeholders through a cloud-based system. In order to achieve these objectives, a design research approach was utilized to develop and evaluate a mobile extended-reality (XR-GIS) application. Validation of the XR-GIS was conducted through a focus group discussion and a questionnaire. The results revealed that 86% of the participants validated the system’s adaptivity to the underground construction. We can conclusively say that this research has produced an efficient solution for data collection and sharing among stakeholders in the underground construction industry.

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Inferring the most probable maps of underground utilities using Bayesian mapping model

Cited by 18 publications

References 22 publications

Pluvial flood risk and opportunities for resilience

Pluvial flood risk and opportunities for resilience

Application of Kinematic GPR-TPS Model with High 3D Georeference Accuracy for Underground Utility Infrastructure Mapping: A Case Study from Urban Sites in Celje, Slovenia

Integrating Geographic Information Systems and Augmented Reality for Mapping Underground Utilities

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