Point clouds are nowadays an indispensable tool in the heritage field, but their actual usage by non-experts engaged with preservation and restoration challenges is often very limited, due to low explainability, human-readability, accessibility and data integrability issues. All of these issues fall under the conceptual umbrella of “understandability”.

AI-based approaches particularly suffer these problems, but, as point clouds, they are an indispensable tool for digital heritage. Other approaches, e.g. based on 3D ontologies, could support understanding aspects but they have been limited addressed.

Therefore the goals of the proposed PhD are:

(i) To study, develop and validate generalisable ontology-based approaches to facilitate the query and use of large and complex 3D heritage point clouds by means of rules able to infer properties and characteristics of a surveyed scene

(ii) To conduct research on novel ways to integrate formal ontologies and AI-based methods to support explainability

(iii) To integrate LLM and NLP models to support 3D heritage understanding

The successful candidate is supposed to have a good ability to connect ICT/AI solutions with heritage needs, along with the agility to successfully prototype innovative, reliable and replicable software solutions.