{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:35:06Z","timestamp":1777606506984,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Iowa Energy Center","award":["#21-IEC-006"],"award-info":[{"award-number":["#21-IEC-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Utility as-built plans, which typically provide information about underground utilities\u2019 position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time.<\/jats:p>","DOI":"10.3390\/s24113559","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:58:00Z","timestamp":1717394280000},"page":"3559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9993-3304","authenticated-orcid":false,"given":"Kunle Sunday","family":"Oguntoye","sequence":"first","affiliation":[{"name":"Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0601-9664","authenticated-orcid":false,"given":"Simon","family":"Laflamme","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA"},{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0702-8351","authenticated-orcid":false,"given":"Roy","family":"Sturgill","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel A.","family":"Salazar Martinez","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David J.","family":"Eisenmann","sequence":"additional","affiliation":[{"name":"Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne","family":"Kimber","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"ref_1","first-page":"165","article-title":"Causes, impacts and costs of strikes on buried utility assets","volume":"168","author":"Metje","year":"2015","journal-title":"Proc. Inst. Civ. Eng.\u2014Munic. Eng."},{"key":"ref_2","unstructured":"Common Ground Alliance (CGA) (2022, October 15). Damage Information Reporting Tool 2021 Analysis and Recommendations. Available online: https:\/\/commongroundalliance.com\/Portals\/0\/2020%20DIRT%20Report_09.29.2021_Final4.pdf?ver=2021-11-03-143123-490."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.tust.2011.10.011","article-title":"Condition assessment of the buried utility service infrastructure","volume":"28","author":"Hao","year":"2012","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"04019078","DOI":"10.1061\/(ASCE)CO.1943-7862.0001724","article-title":"Reducing damage to underground utilities: Lessons learned from damage data and excavators in North Carolina","volume":"145","author":"Panzer","year":"2019","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103229","DOI":"10.1016\/j.autcon.2020.103229","article-title":"3D mapping from partial observations: An application to utility mapping","volume":"117","author":"Dou","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"04019018","DOI":"10.1061\/(ASCE)SC.1943-5576.0000441","article-title":"Reducing Damages to Underground Infrastructure: Performance Evaluation of One-Call Notification Program","volume":"24","author":"Panzer","year":"2019","journal-title":"Pract. Period. Struct. Des. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101370","DOI":"10.1016\/j.compenvurbsys.2019.101370","article-title":"Automatic mapping of urban wastewater networks based on manhole cover locations","volume":"78","author":"Chahinian","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0886-7798(01)00058-X","article-title":"Use of approximate reasoning techniques for locating underground utilities","volume":"16","author":"Lanka","year":"2001","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_9","first-page":"343","article-title":"Mapping of Power Distribution Network using Geographical Information System (G.I.S.)","volume":"2","author":"Hassan","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_10","first-page":"28","article-title":"Feature extraction and automatic material classification of underground objects from ground penetrating radar data","volume":"2014","author":"Lu","year":"2014","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jeong, H., Arboleda, C., and Abraham, D. (2003). Imaging and Locating Buried Utilities, Purdue University. FHWA\/IN\/JTRP-2003\/12.","DOI":"10.5703\/1288284313237"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Oguntoye, K.S., Laflamme, S., Sturgill, R., and Eisenmann, D.J. (2023). Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities. Sensors, 23.","DOI":"10.3390\/s23094367"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1061\/(ASCE)CO.1943-7862.0000269","article-title":"Knowledge-Enabled Decision Support System for Routing Urban Utilities","volume":"137","author":"Osman","year":"2011","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_14","first-page":"1","article-title":"Probabilistic Mixture Model for Mapping the Underground Pipes","volume":"13","author":"Zhou","year":"2019","journal-title":"A.C.M. Trans. Knowl. Discov. Data"},{"key":"ref_15","unstructured":"Chen, H., and Cohn, A.G. (2011, January 16\u201322). Buried Utility Pipeline Mapping Based on Multiple Spatial Data Sources: A Bayesian Data Fusion Approach. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.autcon.2015.03.011","article-title":"Uncertainty-aware geospatial system for mapping and visualizing underground utilities","volume":"53","author":"Li","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jappgeo.2018.01.006","article-title":"Inferring the most probable maps of underground utilities using Bayesian mapping model","volume":"150","author":"Bilal","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"04020013","DOI":"10.1061\/(ASCE)CP.1943-5487.0000892","article-title":"Fusing Heterogeneous Information for Underground Utility Map Generation Based on Dempster-Shafer Theory","volume":"34","author":"Cai","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, X.L., Gabrilovich, E., Heitz, G., Horn, W., Murphy, K., Sun, S., and Zhang, W. (2015). From Data Fusion to Knowledge Fusion. arXiv.","DOI":"10.1145\/2623330.2623623"},{"key":"ref_20","unstructured":"(2022, December 20). Highway\/Utility Guide, Available online: https:\/\/www.fhwa.dot.gov\/utilities\/010604.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.tust.2007.04.002","article-title":"Mapping the Underworld\u2014State-of-the-art review","volume":"22","author":"Metje","year":"2007","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_22","unstructured":"(2024, March 01). What Else Is Down There?|Proceedings|Vol No. Available online: https:\/\/ascelibrary.org\/doi\/abs\/10.1061\/9780784485033.052."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Al-Bayati, A.J., and Kinter, A. (2022). Subsurface Utility Engineering in Practice: Scope of Service Focus. Pipelines, American Society of Civil Engineers.","DOI":"10.1061\/9780784484272.020"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"04016074","DOI":"10.1061\/(ASCE)CO.1943-7862.0001199","article-title":"Integrating Natural Language Processing and Spatial Reasoning for Utility Compliance Checking","volume":"142","author":"Li","year":"2016","journal-title":"J. Constr. Eng. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:52:07Z","timestamp":1760107927000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":24,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113559"],"URL":"https:\/\/doi.org\/10.3390\/s24113559","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]}}}