Situation-aware Cyber–Physical–Social System for Cultural Heritage

Abstract

The safeguard of cultural heritage (CH) is one of the most of interest issues for all the countries, like Italy, known for their thousand-year history. Cultural properties have to be maintained regularly and effectively so that the condition of such properties remains good at all times. Human operators have always been the ones in charge of monitoring and maintaining these properties, with domain experts capable of understanding when and how the maintenance has to be done. In our paper, we define a CH asset as a Cyber–Physical–Social System. We designed and proposed a prototype of a Situation-aware Cyber–Physical–Social System (CPSS) for Cultural Heritage, capable of supporting the human operator situation awareness. The CPSS is a Machine Learning (ML) and expert based system equipped with modules for capturing information, which are then processed with ML techniques to identify asset maintenance issues, understanding how they will evolve, and what are the priorities in the maintenance activity to be performed. We propose three case studies relating respectively to: four structures in the archaeological site of Pompeii, three in the archaeological site of Paestum, and three related to the area the archaeological site of the Colosseum, in Rome, for the safeguarding of which the system uses vulnerability indexes, calculated using prior knowledge related to these structures, maintenance issues detected from aerial photos using a YoloV7 detection model, and context space theory with weather and anthropogenic flow data. We showed how it was possible to identify critical and dangerous situations for these zones, with vulnerability indexes capable of mitigating damaged and dangerous areas to be left in that state with the advent of adverse weather phenomena, which indeed from the photos appeared damaged and flooded.