Comparative Analyses Between Sensors and Digital Data for the Characterization of Historical Surfaces

The paper investigates the use of radiometric and colorimetric data derived from terrestrial laser scanning for the characterization and interpretation of historical architectural surfaces. The research focuses on the relationship between intensity values, materials, construction techniques, and surface decay pathologies, exploring how reflectance data can support semantic segmentation and machine learning classification processes in cultural heritage documentation. Through comparative analyses between different laser scanner sensors and manual segmentation of point clouds, the study evaluates the reliability of intensity values as discriminative features for material recognition and conservation assessment. The workflow integrates point cloud processing, histogram analysis, comparative diagrams, and surface feature interpretation to support future AI-assisted classification and Scan-to-BIM applications for heritage conservation.