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This paper presents an approach for documenting historical phases in architectural heritage by implementing unsupervised Machine Learning (ML). We employ the RANSAC algorithm for architectural segmentation and K-means to analyze the historical sequence in point clouds using geometric features. Finally, the Extended Matrix (EM) tool within a native IFC environment is used for the archaeological metadata linkage standardization. We have tested our approach using several constructive elements of the San Isidoro complex, in León (Spain).

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AI-Driven Analysis in Point Clouds for Archaeological Documentation

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Jesús Muñoz-Cádiz,
  • Ramona Quattrini,
  • Rafael Martín-Talaverano

摘要

This paper presents an approach for documenting historical phases in architectural heritage by implementing unsupervised Machine Learning (ML). We employ the RANSAC algorithm for architectural segmentation and K-means to analyze the historical sequence in point clouds using geometric features. Finally, the Extended Matrix (EM) tool within a native IFC environment is used for the archaeological metadata linkage standardization. We have tested our approach using several constructive elements of the San Isidoro complex, in León (Spain).