Inferring the most probable maps of underground utilities using Bayesian mapping model
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1029
- Type
- D - Journal article
- DOI
-
10.1016/j.jappgeo.2018.01.006
- Title of journal
- Journal of Applied Geophysics
- Article number
- -
- First page
- 52
- Volume
- 150
- Issue
- -
- ISSN
- 0926-9851
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The underpinning research was supported by EPSRC grants (EP/F06585X/1, EP/K021699/1). It addresses the inability of existing technologies to deal with 3D segments and diverse utility types when locating subterranean utilities. For the first time, an approach is proposed for the automated re-construction of 2D/3D maps incorporating Bayesian Mapping with knowledge from heterogeneous sensors and statutory records. The model was validated in controlled/real sites, and test results indicated the success of the approach in handling mapping issues related to the real-time location of buried assets, and curved/3D-shapes.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
