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

Bilal, M, Khan, W, Muggleton, J et al. (5 more authors) (2018) Inferring the most probable maps of underground utilities using Bayesian mapping model. Journal of Applied Geophysics, 150. pp. 52-66. ISSN 0926-9851

Abstract

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Item Type: Article
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Copyright, Publisher and Additional Information:

© 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Keywords: MTU sensors; Most probable maps; Bayesian data fusion; Image processing; Bayesian regression
Dates:
  • Accepted: 11 January 2018
  • Published (online): 31 January 2018
  • Published: March 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
Funder
Grant number
EPSRC
EP/F06585X/1
Depositing User: Symplectic Publications
Date Deposited: 29 Jan 2018 15:59
Last Modified: 21 Jul 2018 20:31
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.jappgeo.2018.01.006
Open Archives Initiative ID (OAI ID):

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