The paper is focused on an ongoing project funded by the Emilia-Romagna region and aimed at the creation of a new workflow finalizing digital data from integrated survey towards an “adaptive” Building Information Modeling (BIM). The project AIM-eBIM—Adapted Information Management for existing Buildings Information Modeling—brings together regional research laboratories and companies to pursue industrial research topics towards a greater deployment of digital tools. Digital surveying has triggered huge potential for innovation, but also generated new challenges in managing and using large amounts of data, often left unused. The quantity of surveyed data used to document built or Cultural Heritage often does not correspond to the quality or reliability of information. Moreover, parametric modeling of existing heritage through BIM is becoming as pervasive as it is necessary, considering regulatory trends. However, these tools can be ineffective from the point of view of users (professionals, companies) who must deal with such complexity. The challenge is to bring discretization (and simplification) processes to source data toward easier informative integration into BIM models, by facilitating and enhancing interpretation needs. In this direction, Artificial Intelligence (AI) algorithms are part of the process. The adapted informative implementation of parametric models is based on digital source data (laser and photogrammetry) segmentation by AI according to specific topics (documentation, analysis, monitoring, conservation, project) and criteria (materials, techniques, components, structures).
Comparative Analyses Between Sensors and Digital Data for the Characterization of Historical Surfaces
The paper investigates the use of radiometric and colorimetric data derived from terrestrial laser scanning for the characterization and interpretation of historical architectural surfaces. The research focuses on the relationship between intensity values, materials, construction techniques, and surface decay pathologies, exploring how reflectance data can support semantic segmentation and machine learning classification processes in cultural heritage documentation. Through comparative analyses between different laser scanner sensors and manual segmentation of point clouds, the study evaluates the reliability of intensity values as discriminative features for material recognition and conservation assessment. The workflow integrates point cloud processing, histogram analysis, comparative diagrams, and surface feature interpretation to support future AI-assisted classification and Scan-to-BIM applications for heritage conservation.
Digital Heritage Documentation. Mapping Features Through Automatic, Critical-Interpretative Procedures
This paper investigates how automatic and semi-automatic digital procedures can improve the documentation, interpretation, and conservation management of cultural heritage through advanced analysis of 3D survey data. The study focuses on the extraction and classification of architectural surface features—such as materials, construction techniques, decay patterns, moisture, deposits, and structural conditions—starting from terrestrial laser scanning and photogrammetric datasets. Particular attention is given to the underused diagnostic potential of laser scanner intensity values, which can reveal variations in material reflectance and hidden anomalies not always visible to the naked eye. The author proposes a workflow combining manual annotation, semantic segmentation, machine learning classification, and integration into H-BIM and semantic web platforms. Existing point clouds are reused as training datasets to define value clusters associated with conservation issues and material categories. The research argues that AI-supported thematic mapping can reduce processing time, improve diagnostic reliability, and transform dense survey models into actionable information systems for heritage managers, restorers, and public administrations.
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.
3D Heritage Data Fruition and Management. Point Cloud Processing for Thematic Interpretation
Technologies and digital tools such as laser scanning and photogrammetry are nowadays widely used in the field of architectural heritage survey, being able of producing 3D models characterized by high metric and morphological accuracy. These databases are becoming essential also for the development of more effective interventions on heritage buildings. Despite the advancement of increasingly automated analytical procedures, the management and analysis of point cloud models can still be quite time-consuming and complex, depending on specific assessments to be carried out. In the direction of optimizing these processing steps, several research is being carried out by applying Artificial Intelligence processes to make predictions based on sample data. The aim of the paper is to analyse point clouds processing focusing on geometric and radiometric features for diagnostic analysis. A specific focus aims at analysing possible in-depth uses of the intensity value as a benchmark for historical surfaces assessment, toward an optimized models’ interpretation and classification of the 3D data points, integrating data and information from different sensors. Point clouds under analysis have been carried out by different acquisition techniques; this provides an interesting opportunity to compare the results in terms of intensity value produced by different sensors. The paper will analyse the State of the Art, also illustrating a set of outcomes obtained by the authors, deepening two specific case studies, in order to outline not only the main background and shortcomings in managing complex database, but also possible innovations pointing out new research questions.
From semantic-aware digital models to Augmented Reality applications for Architectural Heritage conservation and restoration
The paper presents the integration of Augmented Reality and Mixed Reality tools for the built Heritage management and control, both remote and on-site, and real time interaction, starting from a preliminary set of experimentation carried out for knowledge and tourism purposes.
Within the framework of these experimentations, specific data inventories are related to the IFC model, and all these data are collected on a cloud-based platform, allowing the “dialogue” among platform and applications. Therefore, BIM integration is the first step of the procedure, considering a workflow where data capturing, digital documentation, and data modeling and aggregation are the entry level to manage applications able to give an added value in gaining the greatest technical benefit from digitization. Mapping the main features and the state of conservation is the second step, including geometric features, historical knowledge, documents and pictures related to materials, diagnostic analysis, etc.
Starting from AR applications developed on several case studies, including historical buildings, museums and a church, aimed at an immersive on-site navigation thanks to a set of additional information related to the digital model, experimentations oriented to technical uses are presented.
An extension of applications for the analysis and interpretation of architectural heritage and technical uses can be an effective support in restoration, conservation and maintenance of historic buildings, by enhancing the real world through virtual objects and creating a new mixed reality environment for technical users.
Comparative Analyses Between Sensors and Digital Data for the Characterization of Historical Surfaces
In the “informational” potential included in 3D digital models obtained by laser scanner survey, several data can be processed in addition to geometric features in order to widen the application of digitization to conservation of Cultural Heritage. Among these data, the intensity value is a (potentially) powerful knowledge concerning the interpretation of surfaces. The visual-comparative analysis developed over years of experimentations demonstrate the need to target research towards comparative data, “sampling” the reflectance of different materials, measured with different sensors and in environments with different boundary conditions. If until recently the process already presented interesting research directions in terms of calibration or control of results on specific materials but difficult at a comparative level, today, thanks to new data segmentation processes and algorithmic procedures, advancements and further comparisons will be possible opening to new interpretative hypotheses. The paper explores ongoing experimentations aimed at comparing different radiometric and colorimetric data obtained by 3D surveying using different laser scanner technologies on historical surfaces, to support the identification of features directly on the 3D model. The goal is to test the link between the intensity value and materials, construction techniques, and decay pathologies, in order to use, in the future, also this parameter as a radiometric feature in machine learning segmentation and classification algorithms. The contribution develops and deepens at the application level the theoretical background and the first experiments carried out on two case studies.