Digital Heritage Documentation. Mapping Features Through Automatic, Critical-Interpretative Procedures

This paper investigates how automatic and semi-automatic digital procedures can improve the documentation, interpretation, and conservation management of cultural heritage through advanced analysis of 3D survey data. The study focuses on the extraction and classification of architectural surface features—such as materials, construction techniques, decay patterns, moisture, deposits, and structural conditions—starting from terrestrial laser scanning and photogrammetric datasets. Particular attention is given to the underused diagnostic potential of laser scanner intensity values, which can reveal variations in material reflectance and hidden anomalies not always visible to the naked eye. The author proposes a workflow combining manual annotation, semantic segmentation, machine learning classification, and integration into H-BIM and semantic web platforms. Existing point clouds are reused as training datasets to define value clusters associated with conservation issues and material categories. The research argues that AI-supported thematic mapping can reduce processing time, improve diagnostic reliability, and transform dense survey models into actionable information systems for heritage managers, restorers, and public administrations.