From Spherical Photos to Facility Management. Algorithmic Optimization Procedures for Buildings in BIM Approaches

Recognizing and modeling architectural characteristics from images and point clouds for managing and visualizing cultural heritage is part of a very active field, now opening to visual scripting and machine learning techniques. This evolution is moving the new frontiers of the digital survey, which represents the starting point for the creation of structured data models of buildings – that is, their “digital twins” – to be used directly, without the need for classic technical drawings. Aiming to participate in automating these processes, we have conducted research to implement automated workflows for digitalizing and managing historical and cultural heritage. In particular, the experimented operation workflow is aimed at the construction of a digital process to relate data acquired in the real context with information tagging on 360° images, linking georeferenced data in the BIM (Building Information Modelling) digital twin, allowing maintenance operators to directly mark the presence of damage by showing it through images. Specifically, the case study for this experimentation is represented by some buildings belonging to the University of Naples Federico II. Following a 360° digital survey and the realization of the Digital Twin, data georeferencing was developed by creating a specific algorithmic information script to automatize tagging in a BIM environment, producing data localization and an alert message on the model.