In recent years, the automatic segmentation and classification of digital survey data has been experimented with in built heritage studies. Despite the encouraging progress in the use of Machine and Deep Learning techniques, the semantic segmentation of point clouds is more complex, especially for the historic environment for which, due to the heterogeneity of shapes, it is more difficult to recognize homogeneous regions with similar properties. Given the need to process a large volume of already annotated data for the training and recognition of new scenes, the type and quality of the initial data play a fundamental role in the classification process, as they influence the subdivision into predefined categories that are not always consistent with a decomposition into architectural elements and sub-elements shared by the scientific community. This is an interpretative problem that already emerges from traditional manual labelling, which, being highly subjective, reduces the reproducibility of the results. The paper focuses on understanding to what extent the recognition of homogeneous regions is influenced by factors such as: manual labelling carried out by annotators with different specializations; density value of the point clouds; and type of data acquired depending on the acquisition sensor. These evaluations were conducted by employing the Random Forest algorithm on specific pre-processed point cloud datasets, with reference to the typology of the Franciscan cloister, in order to make the recognition flows more controlled and less ambiguous, aiming at an advancement towards more efficient modelling and management of the existing architectural heritage.
Predicting Architectural Decay by AI Applied to 3D Survey
The paper presents a predictive framework for monitoring architectural decay by integrating 3D survey data with artificial intelligence techniques. The methodology is structured as a Digital Decision System (DDS) that combines multi-source data acquisition (laser scanning, photogrammetry, mobile mapping, and low-cost sensors) with machine learning models to forecast degradation trends. Following a Knowledge Discovery in Database (KDD) pipeline—comprising data collection, preprocessing, transformation, data mining, and interpretation—the system uses linear regression to model the relationship between temporal data and measurable degradation phenomena. The approach is validated through experimental applications, demonstrating how predictive models can support conservation strategies, optimize monitoring processes, and assist decision-making in architectural restoration.
Dataspace: Predictive Survey as a Tool for a Data Driven Design for Public Space
This paper proposes a data-driven methodological framework for the analysis, simulation, and design of public space through predictive survey techniques. The research integrates digital survey methods, big data acquisition, and artificial intelligence to construct a three-dimensional model capable of supporting decision-making processes in urban design. The approach is structured into four phases: historical and contextual analysis, morphometric data acquisition through photogrammetry and LiDAR, digital model construction, and predictive simulation.
The study develops a digital twin environment in which heterogeneous data—collected from field surveys, low-cost sensors, and human-generated sources—are integrated and analyzed. As illustrated in the workflow diagram (Fig. 1, p. 705), the system combines physical and digital datasets to simulate future scenarios and evaluate public space quality. The methodology incorporates both quantitative metrics and qualitative assessments based on Gehl’s criteria, enabling the interpretation of spatial use, environmental conditions, and user behavior. The results demonstrate that predictive survey, combined with IoT and machine learning techniques, can support the design of responsive and adaptive urban environments, while highlighting current limitations in data acquisition, integration, and systematization.
Virtual Canova: a Digital Exhibition Across MANN and Hermitage Museums
The paper presents the results of a scientific collaboration between the Interdepartmental Research Center Urban/Eco of the University of Naples Federico II and the MANN (Museo Archeologico Nazionale di Napoli, National Archaeological Museum of Naples).The research activity was aimed to the digitisation, design, and development of an AR/VR-powered narrative experience regarding Antonio Canova’s statuary that is currently exhibited at the MANN, loaned by the Hermitage in St. Petersburg: Cupid, Hebe, Dancer, Cupid and Psyche, the Genius of Death and The Three Graces.The project is motivated by the will to realize an active example of a digital museum, where cultural and formative experiences related to the fruition of architectural and artistic artifacts can be relived over time, even when manufacts are not physically and/or temporally located in the space where the experience takes place.
CHROME Project: Representation and Survey for AI Development
The paper shows the results of the PRIN CHROME Cultural Heritage Orienting Multimodal Experi-ences project, about the three charterhouses of Campania, with a specific focus on research activities related to the connections between representation, survey, AI and VR. The project has formalized a methodology of collection, analysis and modeling of multimodal data, useful for designing virtual agents in 3D environments, which can be applicable in museum environments. The achievement of the goal is pursued through: (i) an integrated range–based acquisition and morphometric data modeling process coherent with VR management, (ii) the use of semantic maps linked with thesauri published as LOD to solve both the theme of ambiguity and annotation uncertainty and the inter-pretability of information by an AI; (iii) the modeling of a virtual agent with the development of a mathematical model for computational control of gestures and prosody.
Semantically Annotated 3D Material Supporting the Design of Natural User Interfaces for Architectural Heritage
With the advent of artificial intelligence and natural user interfaces, the need for multimedia material that can be semantically interpreted in real time becomes critical. In the field of 3D architectural survey, a significant amount of research has been conducted to allow domain experts represent semantic data while keeping spatial references. Such data becomes valuable for natural user interfaces designed to let non-expert users obtain information about architectural heritage. In this paper, we present the architectural data collection and annotation procedure adopted in the Cultural Heritage Orienting Multimodal Experiences (CHROME) project. This procedure aims at providing conversational agents with fast access to fine-detailed semantic data linked to the available 3D models. We will discuss how this will make it possible to support multimodal user interaction and generate cultural heritage presentations.
Segmentation protocols in the digital twins of monumental heritage: a methodological development
The paper shows an advancement of the research that the authors have been carrying out in recent years in semantic structuring of digital architectural representations field, with a focus on the issue of uncertainty of annotations. The studies carried out in this regard have shown how the domain experts specialization determines a vision and interpretation of the same architectural object that we could define “categorized”. The interest was, then, in verifying which categories of experts have a greater degree of agreement in classifying and segmenting architectural elements, to highlight which specializations contribute the most in enriching the semantic reasoning about such forms. Aiming to broaden this reasoning, the research was deepened with annotation sessions concerning architecture examples that didn’t correspond to the classical orders rule but included wider fields of historical heritage (from sacred to fortified architecture). The aim is to verify whether the uncertainty of annotation is actually ascribable to a specific segment of the historical heritage, for example, the classical world, or whether the question is broader and as such in needs deeper thinking.