Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets

In Architectural Heritage (AH), diagnostics represent a key topic, related to the assessment of the state of conservation, for a well-structured and integrated knowledge about the artefact. This is of paramount importance for buildings denoted by historic-artistic interest, often in widespread damaged conditions. Digital technologies could represent a valuable alternative, for the creation, processing, and analysis of 2D images or reality-capture 3D data. Indeed, image processing and artificial intelligence are spreading in the AH domain, for both documentation and conservation purposes. The present research proposes a novel methodological approach, based on the application of machine learning systems, for the assessment and mapping of decay evidence in an ancient artefact, within the diagnostic process. An unsupervised hierarchical clustering of point clouds has been validated and compared against a state-of-the-art supervised random forest. The presented pipeline has been tested on a plurality of case studies, representative of the territorial historic built heritage, qualified by a great variety, in terms of age, building type, surface, and lighting conditions. For most of the classes, the cloud-based unsupervised approach consents to accomplish better performances.

Automated summary

This research proposes a novel method for assessing and mapping decay evidence in ancient artifacts using machine learning systems for unsupervised hierarchical clustering, compared to a state-of-the-art supervised random forest. The presented approach achieves better performance in a variety of case studies.