In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.

Abandoned industrial sites are generally characterized by soil and subsoil contamination. The paradigm currently employed for their remediation is “tabula rasa”, i.e., remediation of the entire site before its repurpose. However, this method is not economically, socially, or technologically sustainable: it delays the reuse of large areas, often well-connected to infrastructures, whose reuse may prevent further soil consumption. A possible solution to this problem is the application of adaptive reuse principles. This study, conducted at FULL (Future Urban Legacy Lab) in Politecnico di Torino, presents an interdisciplinary approach to spatialize, visualize, and manage interactions between reclamation and urban design for the transformation of contaminated urban areas. The core is based on a decision support parametric toolkit, named AdRem, developed to compare available remediation techniques and schematic urban design solutions. AdRem uses a 3D modeling interface and VPL scripting. Required input data are a geometric description of the site, data on the contamination status, viable remediation techniques, and associated features, and schematic urban design recommendations. A filtering process selects the techniques compatible with the site use foreseen. The output is an optimized remediation and reuse plan that can support an interdisciplinary discussion on possible site regeneration options.

This paper presents the most recent developments in a project aimed to the documentation, storage and dissemination of the cultural heritage. The subject of the project are more than 70 Baroque atria in Turin, recognized by critics for their particular unitary vaulted systems Our research team is currently working on digitizing documents and studying ways to enhance and share these results through ICT. In particular, we want to explore possibilities for recognizing and tracing three-dimensional objects in augmented reality (AR) applications connected to the collected data. Recent developments in this field relate to the technology available on widespread mobile devices such as tablets and smartphones, allowing for real-time 3D scanning. Using software prototypes, we want to introduce some problems involved in integrating this technology into digital archives.

This research is realised in the framework of a project recently funded as part of the PNRR (National Recovery and Resilience Plan) in the Accessibility sector. The working team has been established in the framework of the scientific agreement between the Museum of Villa della Regina in Turin, the Department of Architecture and Design at Politecnico di Torino, and the Department of History, Drawing and Restoration of Architecture at Sapienza Università di Roma, and includes knowledge from art history, digital surveying, 3D modelling, and digital solutions for cultural heritage. The research involves the reconstructive 3D modelling of Piffetti’s Library, once placed in the cabinet toward midnight and west inside the Villa della Regina and today in the Palazzo del Quirinale, and its interactive visualisation through augmented reality (AR) and virtual reality (VR) aimed at accessibility.