Machine Learning in Architectural Surveying: Possibility or Next Step of Development? From Photogrammetry to Augmented Reality of a Sculptural Group

This paper investigates the future role of machine learning in architectural surveying workflows, focusing on the digitization, optimization, and dissemination of sculptural heritage through photogrammetry and augmented reality. The study is based on the Sacred Mount of San Vivaldo in Tuscany, a sixteenth-century devotional complex composed of chapels containing terracotta statuary groups. Extensive image-based surveys were carried out using high-resolution DSLR photography to generate dense photogrammetric models of the sculptures. The authors discuss how machine learning could support multiple stages of the process, including automated image acquisition, quality control, mesh simplification, texture baking, and adaptive optimization for AR platforms and 3D printing. Particular attention is given to the challenge of converting highly detailed survey models into lightweight yet accurate assets suitable for mobile visualization and public interaction. The research also proposes AR applications capable of enriching the visitor experience with historical information and interactive content. The study concludes that AI-assisted surveying can become a major next step in heritage documentation, reducing manual effort while improving accessibility, reuse, and communication of complex cultural assets.

Fragments of Stories and Arts: Hidden and not so Hidden Stories

Any city with a long and articulated past has buildings, squares and monuments linked to its history, the built heritage is its more evident direct link to the historical and artistic events that characterize the present urban asset. In between this main feature, there is the possibility that a myriad of a minor, medium or minimal sized elements may be present, creating a network of evidence, sometimes diffi-cult to catch, but strongly connected to past events and valuable stories. It brings to light details that are often ignored or misinterpreted because of their historical peculiarities. The present research is focused on a structure based on an AR solution to make these traces in Florence downtown more ac-cessible and discoverable. This paper base is the starting point for a special and fascinating exploration of the Florentine downtown, passing by a series of “secondary” but highly intriguing traces. In addition to the most important places and monuments, known and desired by tourists, there are details and trivia that further enhance the uniqueness of the experience in the historical and cultural city.