The paper addresses the challenges related to the application of machine learning solutions to support historical and architectural critical interpretation. The case study reported here is the complex of San Lorenzo in Miranda, located in the Roman Forum along the Via Sacra. The structure, originally conceived as a temple and later transformed into a church, is a multi-layered architectural palimpsest in which each construction phase, at least since Roman times, has inevitably influenced the subsequent modifications. The building’s rebirth upon itself through the integration and modification of its older portions is its main characteristic, imparting a specific complexity and interest. The first phase of the research focused on bibliographic study and 3D digital survey, both of which contributed to identifying the multiple construction phases of the building. In particular, the digital survey was implemented through two survey campaigns. The first involved a massive 3D laser scanner acquisition and UAV photogrammetric capturing, while the second integrated a topographic survey to georeference the captured data. The second phase concerned data interpretation, focusing on research questions related to a hypothetical reconstruction of the cella’s original wall covering layer. This question was addressed by leveraging machine learning algorithms used to automatically identify the covering traces.
Representation Advances And CHallenges association