Detection and localization of underground networks by fusion of electromagnetic signal and GPR images

Hafsi, Meriem; Bolon, Philippe; Dapoigny, Richard

doi:10.1117/12.2266946

Cited by 3 publications

(1 citation statement)

References 7 publications

(6 reference statements)

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“…Moreover, the authors in [15] use a Bayesian mapping model to integrate knowledge extracted from sensors' raw data and available statutory records to infer underground network data including water pipes. To enhance the detection of underground networks, [16] fuse the data collected from different radars. In [17], the authors apply deep neural networks to detect the position of manhole covers from high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Bel-Ghaddar

Chahinian

Seriai³

et al. 2021

Water

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Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

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Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Bel-Ghaddar

Chahinian

Seriai³

et al. 2021

Water

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States of Practice and Research on Applying GPR Technology for Labeling and Scanning Constructed Facilities

Xiang

Rashidi

2019

J. Perform. Constr. Facil.

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Combining model-driven engineering and sewerage networks: towards a generic representation

Bel-Ghaddar

Seriai²,

Begdouri

et al. 2020

2020 6th IEEE Congress on Information Science and Technology (CiSt)

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Representing and processing digital data related to underground networks, particularly sewerage networks, is increasingly becoming a priority for the managers of these networks. Indeed better representation would allow them, among others, to improve knowledge and to take the best decisions regarding these generally poorly identified infrastructures. The heterogeneity of data and the multiplicity of data models representing sewerage networks, often specific to each operator, as well as the imperfections associated with both the available data and those collected from different sources, generate complexity in terms of on-the-field interventions efficiency. They also highlight the need for aggregation (unification), control and analysis. The main objective of our work is to merge multisource data to obtain more precise and complete digital maps of sewerage networks. In this paper, we propose a generic data modelling for data fusion purposes taking into consideration the uncertainty aspects related to the collected data by allowing a confidence value for each data source and for each single data provided by a source.

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Detection and localization of underground networks by fusion of electromagnetic signal and GPR images

Cited by 3 publications

References 7 publications

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

States of Practice and Research on Applying GPR Technology for Labeling and Scanning Constructed Facilities

Combining model-driven engineering and sewerage networks: towards a generic representation

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