Machine Learning Techniques for Enhanced Detection of Underground Infrastructure in Urban Environments

Renát Haluška (), Zuzana Sokolová, Maroš Harahus, Marianna Koctúrová, Slávka Harabinová, Štefan Gorás, Michal Gorás and Ján Domanický
Additional contact information
Renát Haluška: Technical University of Kosice
Zuzana Sokolová: Technical University of Kosice
Maroš Harahus: Technical University of Kosice
Marianna Koctúrová: Technical University of Kosice
Slávka Harabinová: Technical University of Kosice
Štefan Gorás: Technical University of Kosice
Michal Gorás: Technical University of Kosice
Ján Domanický: Technical University of Kosice

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 336-344 from Springer

Abstract: Abstract This research investigates the application of machine learning techniques to enhance the detection of underground infrastructure in urban environments. Our study aims to improve the accuracy, efficiency, and cost-effectiveness of underground infrastructure detection by leveraging the capabilities of machine learning. Through the integration of advanced data processing techniques and geospatial information, we develop novel methodologies for subsurface mapping and localization. We analyze the strengths, limitations, and practical considerations of employing machine learning in underground infrastructure detection. The findings of this research contribute to the advancement of modern detection methods and provide valuable insights for urban planning, infrastructure management, and disaster preparedness efforts.

Keywords: Anomaly Detection; GPR; Machine Learning; Underground Infrastructure Detection (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_37

Ordering information: This item can be ordered from
http://www.springer.com/9783031753299

DOI: 10.1007/978-3-031-75329-9_37

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().