Inferring the most probable maps of underground utilities using Bayesian mapping model
Inferring the most probable maps of underground utilities using Bayesian mapping model
Mapping the Underworld (MTU), a major initiative in the UK, is focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing a multi-sensor mobile device. The aim of MTU device is to locate different types of buried assets in real time with the use of automated data processing techniques and statutory records. The statutory records, even though typically being inaccurate and incomplete, provide useful prior information on what is buried under the ground and where. However, the integration of information from multiple sensors (raw data) with these qualitative maps and their visualization is challenging and requires the implementation of robust machine learning/data fusion approaches. An approach for automated creation of revised maps was developed as a Bayesian Mapping model in this paper by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment-manhole connections. The model consisting of image segmentation algorithm and various Bayesian classification techniques (segment recognition and expectation maximization (EM) algorithm) provided robust performance on various simulated as well as real sites in terms of predicting linear/non-linear segments and constructing refined 2D/3D maps.
Bayesian data fusion, Bayesian regression, Image processing, Most probable maps, MTU sensors
52-66
Bilal, Muhammad
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Khan, Wasiq
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Muggleton, Jennifer
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Rustighi, Emiliano
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Jenks, Hugo
b327c663-2e45-471b-8611-e8e8e304d63e
Pennock, Steve R.
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Atkins, Phil R.
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Cohn, Anthony
314421b2-974f-464d-93db-2b4e1db87204
1 March 2018
Bilal, Muhammad
ed712fa1-28ee-469a-9369-f57d5f527e98
Khan, Wasiq
d6d10b13-694e-42d2-87fe-3184379e8053
Muggleton, Jennifer
2298700d-8ec7-4241-828a-1a1c5c36ecb5
Rustighi, Emiliano
9544ced4-5057-4491-a45c-643873dfed96
Jenks, Hugo
b327c663-2e45-471b-8611-e8e8e304d63e
Pennock, Steve R.
e9daf224-cb16-42e1-988e-cb60a6207b77
Atkins, Phil R.
dc1d4ac2-635c-4ce2-bbc4-80186d042921
Cohn, Anthony
314421b2-974f-464d-93db-2b4e1db87204
Bilal, Muhammad, Khan, Wasiq, Muggleton, Jennifer, Rustighi, Emiliano, Jenks, Hugo, Pennock, Steve R., Atkins, Phil R. and Cohn, Anthony
(2018)
Inferring the most probable maps of underground utilities using Bayesian mapping model.
Journal of Applied Geophysics, 150, .
(doi:10.1016/j.jappgeo.2018.01.006).
Abstract
Mapping the Underworld (MTU), a major initiative in the UK, is focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing a multi-sensor mobile device. The aim of MTU device is to locate different types of buried assets in real time with the use of automated data processing techniques and statutory records. The statutory records, even though typically being inaccurate and incomplete, provide useful prior information on what is buried under the ground and where. However, the integration of information from multiple sensors (raw data) with these qualitative maps and their visualization is challenging and requires the implementation of robust machine learning/data fusion approaches. An approach for automated creation of revised maps was developed as a Bayesian Mapping model in this paper by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment-manhole connections. The model consisting of image segmentation algorithm and various Bayesian classification techniques (segment recognition and expectation maximization (EM) algorithm) provided robust performance on various simulated as well as real sites in terms of predicting linear/non-linear segments and constructing refined 2D/3D maps.
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Accepted/In Press date: 11 January 2018
e-pub ahead of print date: 31 January 2018
Published date: 1 March 2018
Keywords:
Bayesian data fusion, Bayesian regression, Image processing, Most probable maps, MTU sensors
Identifiers
Local EPrints ID: 419499
URI: http://eprints.soton.ac.uk/id/eprint/419499
ISSN: 0926-9851
PURE UUID: b11428ca-a305-413b-9487-5a77c25b4ee6
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Date deposited: 13 Apr 2018 16:30
Last modified: 17 Mar 2024 12:02
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Contributors
Author:
Muhammad Bilal
Author:
Wasiq Khan
Author:
Hugo Jenks
Author:
Steve R. Pennock
Author:
Phil R. Atkins
Author:
Anthony Cohn
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