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

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • 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)

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.

Suggested Citation

  • Renát Haluška & Zuzana Sokolová & Maroš Harahus & Marianna Koctúrová & Slávka Harabinová & Štefan Gorás & Michal Gorás & Ján Domanický, 2024. "Machine Learning Techniques for Enhanced Detection of Underground Infrastructure in Urban Environments," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 336-344, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_37
    DOI: 10.1007/978-3-031-75329-9_37
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