The contribution related to the processes of knowledge and enhancement of cultural heritage, based on the potential offered by Artificial Intelligence and Augmented Reality, proposes an analysis of the sit-uation of L’Aquila’s buildings ten years after the 2009 earthquake. The research, conducted on the basis of integrated surveys carried out before and after the earthquake, focuses on the application of AR/ VR devices through the implementation of AI. The aim is to propose a solution able to promote the use of the historical buildings of L’Aquila, activating at the same time the dynamics of cultural regeneration in the area, through the use of an App that uses AR+AI systems; a tour that can show with immediacy the urban architectural evolution, solving in part the intrinsic difficulties posed by the current situation of precarious visibility and accessibility of some noble buildings, subject to restoration, with the aim of highlighting the evolution of transformations, stylistic and structural changes produced by the different and stratified post–earthquake reconstructions.
Augmenting Angri: Murals in AR for Urban Regeneration and Historical Memory
Augmenting Angri is a project aiming at preserving the memory of about twenty murals located in the historical centre of the small city of Angri (SA) using ICT and in particular Augmented reality. The project has been carried on in 2020 with the students attending the integrated course of Visual Expression (professors A. Pagliano and P. Vitolo) in the Master’s Degree Course in Design for the Built Environment. Augmenting Angri consists of an innovative valorisation path in Augmented reality which expand the narrative message of the old murales thanks to the digital contents (audio, video, animations and 3D models), sometime replacing them back, in case of totally disappeared murales.
Augmented Reality as a Research Tool, for the Knowledge and Enhancement of Cultural Heritage
Starting from a reflection on the new role of digital technology in this pandemic situation, and partic-ularly the role of AR for Cultural Heritage, this paper presents two AR applications. The first is used to share the analyses carried out on the Pantheon surveys realized between the seventeenth and nineteenth centuries. The second is about the illusionistic dome in the Church of Sant’Ignazio in Rome, realized by Andrea Pozzo. These two applications can display three–dimensional models superimposed onto images and original drawings, and also two–dimensional contents: the results of graphical analysis carried out on digital copies. The final aim of this ongoing research is to develop some AR multimedia content, linked to semantic concepts.
New Interpretative Models for the Study of Urban Space
The research project aims to acquire data on the impact of an environment on humans. The pro-cess is based on the use of the EEG helmet synchronised to GPS: the EEG helmet is a device that non–invasively records 14 channels of the human brain through electrodes and, through an algorithm, transforms them into cognitive states in real time. Starting from this raw data, through the ‘circumplex model’, cognitive states can be transformed into emotions with the aim of showing what people on average feel in a given urban space. The data, which are linked through GPS to a position in space, are empirically recorded with statistically significant samples from which emotions are reprojected onto the mesh of the digitally reconstructed environment through a process of photomodelling.
AR+AI = Augmented (Retail + Identity) for Historical Retail Heritage
From the cafés in the dreamlike square in Rimini depicted by Fellini in Amarcord to the movie theater in the village of Giancaldo depicted by Tornatore in Nuovo Cinema Paradiso, historic shops represent a rich and significant cultural heritage. The protection of historic shops is often the subject of specific dedicated regulations throughout the country, aimed at preserving their compositional identity and preventing the impoverishment of their urban image. In this context, starting from the analysis of future scenarios in the field of retail design, the proposed contribution aims to open new perspec-tives on the potential offered by the use of AR and AI technologies in the identity enhancement of the architectural heritage of historic shops. The idea, presented through a real case study, is that the concept of Augmented Retail can represent a strategy to achieve an Augmented Identity, changing the retail space from a ‘product window’ to an ‘experiential theatre’.
Rebuilding Amatrice. Representation, Experience and Digital Artifice
The Diocesan Museum of Amatrice, razed to the ground by the 2016 earthquake, exhibited works of sacred art but also materials on the building, the former church of Sant’Emidio, and on the territory. The grave loss provokes a reflection on the psychological significance of heritage, the lack of which creates disorientation among the population. Technologies help to regenerate the bond with the ter-ritory, to search for one’s own identity not only in the tangible dimension, which is hardly visible today, but also in the relationship between objects, stories and meanings to be reconstructed. The research proposes a path of re–appropriation that identifies the semantic domains of the community and associates them in the territory through the dissemination of traces: images and stories are the new points of contact georeferenced around the voice of the protagonists. Two multimedia installations emphasize the evocative interaction between reality, memory and reproduction of the intangible to promote the common good of collective memory.
Photogrammetric Survey for a Fast Construction of Synthetic Dataset
In this work we show how Physically Based Rendering (PBR) tools can be used to extend the training image datasets of Machine Learning (ML) algorithms for the recognition of built heritage. In the field of heritage valorization, the combination of Artificial Intelligence (AI) and Augmented Reality (AR) has allowed to recognize built heritage elements with mobile devices, anchoring digital products to the physical environment in real time, thus making the access to information related to real space more intuitive and effective. However, the availability of training data required for these systems is extremely limited and a large–scale image dataset is required to achieve accurate results in image recognition. Manually collecting and annotating images can be very resource and time–consuming. In this contribution we explore the use of PBR tools as a viable alternative to supplement an otherwise inadequate dataset.
Machine Learning for Cultural Heritage Classification
Cultural Heritage (CH) assets may be defined as integrated spatial systems composed of inter-connected shapes. The classification and organization of geometries within a hierarchical system are functional to their correct interpretation, which is often performed using 3D point clouds. The recurring shapes recognition becomes a crucial activity, nowadays accelerated by Machine Learning (ML) procedures able to associate semantic meaning to geometric data. An interdisciplinary research team [1] has developed a ML supervised approach, tested on the Milan Cathedral and Pomposa Abbey datasets, which presents an innovative multi–level and multi–resolution classification (MLMR) process. The methodology improves the learning activity and optimizes the 3D classification by a hierarchical concept.
From AI to H–BIM: New Interpretative Scenarios in Data Processing
The paper results from preliminary research experiences focused on the use of Artificial Intelligence as a tool for processing the large amount of data that can be obtained from digitisation processes ap-plied to the Architectural Heritage. The new interpretative scenarios will be outlined starting, on one hand, from a series of consolidated experiences in the field of three–dimensional survey, modelling and semantic enrichment, and, on the other, from the use of Augmented Reality tools for the fruition of the Heritage itself.The research aims to further investigate the great potential of processing point cloud models using Artificial Intelligence, to extrapolate, from the digitized data, information levels that go beyond shapes, offering better integration within the Building Information Modeling environment.
Image–Based Modelling Restitution: Pipeline for Accuracy Optimisation
The paper presents an innovative approach to support survey methods by applying AI algorithms to improve the accuracy of point clouds generated from UAV imagery. The work analyses different photogrammetric accuracy parameters in a first step, such as reprojection error and the intersection angle between homologous rays, verifying that a single parameter is enough to evaluate the accuracy of the photogrammetric restitution. Therefore, some of the calculated parameters were analysed through a Self–Organizing Map (SOM) to reach a compromise between the value of the variables analysed and the noise reduction associated with the 3D model definition. In the case study, it has been observed that the parameter that most influences the noise in the photogrammetric point clouds is the intersection angle.