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AI methods for enhancing and recognizing archaeological features in heterogeneous geophysical datasets.

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Summary

This study introduces a new method combining Ground Penetrating Radar (GPR) and magnetic gradiometry (MAG) with Artificial Intelligence (AI) for better archaeological feature detection. The approach enhances data interpretation and improves the reliability of identifying subsurface remains.

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Area of Science:

  • Archaeological geophysics
  • Artificial Intelligence in archaeology
  • Geospatial analysis

Background:

  • Near-surface geophysical surveys like Ground Penetrating Radar (GPR) and magnetic gradiometry (MAG) are crucial for archaeological site investigation.
  • Interpreting complex and heterogeneous geophysical data presents significant challenges.
  • Automated methods are needed to improve the efficiency and reliability of archaeological feature detection.

Purpose of the Study:

  • To present a methodological framework for enhancing the detection and interpretation of archaeological features.
  • To develop a combined approach using spatial analysis and Artificial Intelligence (AI) for automatic feature enhancement.
  • To validate the proposed method using real-world geophysical survey data.

Main Methods:

  • Integration of Ground Penetrating Radar (GPR) and magnetic gradiometry (MAG) data.
  • Application of spatial analysis techniques.
  • Utilisation of Self-Organizing Maps (SOM), a type of Artificial Intelligence, for automatic feature enhancement and recognition.

Main Results:

  • The combined approach significantly improved the readability of complex geophysical datasets.
  • Enhanced reliability in the interpretation of archaeological findings.
  • Successful identification and facilitation of interpretation for subsurface remains in the Grumentum archaeological area.

Conclusions:

  • The proposed methodological framework effectively enhances archaeological feature detection and interpretation.
  • The integration of GPR, MAG, and AI (SOM) offers a powerful tool for subsurface archaeological prospection.
  • The approach shows potential for broad application in other archaeological contexts and related fields.