The concept of HBIM (Historic/Heritage Building Information Modeling) has attracted growing interest within research communities in recent years, as reflected in an expanding body of literature exploring its potential in data acquisition and modeling, historical evolution documentation, heritage management, and condition analysis. Yet, new challenges arise in extended HBIM capabilities by integration and interoperability with other technologies and environments for comprehensive heritage assessment. In this context, this paper presents a scoping review, based on the PRISMA protocol, of 60 publications from the Scopus database that document research frameworks and applications of IDPs (integrated digital platforms), where HBIM is combined with different systems to enhance data richness, functionality, and analytical evaluation, as well as to exchange, interpret, and use information effectively. The results show three major thematic areas, namely multi-scale analyses based on HBIM and GIS (geographic information systems); multi-source data repositories development; and sensor networks integration with advanced IoT (Internet of Things) systems. The overview outlines how these frameworks foster the development of interoperable, multi-layered, and data-driven ecosystems, advancing HBIM to an operational component in heritage management and enabling predictive diagnostics and real-time monitoring, while current limitations in semantic consistency, automation, and scalability still hinder full implementation.

Cultural Heritage has been significantly impacted by advancements in the Information and Communications Technology domains, which have inspired a strong multidisciplinary interest and enabled the development of innovative strategies for the preservation, management, and enhancement of the heritage itself. Notably, the digitisation process, which entails the acquisition of 3D data obtained through cutting-edge LiDAR and photogrammetric scanning techniques, is set up as an advantageous tool for producing an accurate representation of the historical buildings. In addition, point clouds and reliable HBIM models have caught the minds of the architectural community, and are now receiving huge backing from Artificial Intelligence. Such support is provided by procedures that link semantic features to structural and decorative elements. In this scenario, the following research is presented: the aim is to test an automated iterative process within a scan-to-BIM methodology, starting from automatic point cloud segmentation operations with open-source, model-fitting algorithms. This method will prove to be a solid support for the final phase of the 3D parametric/adaptive reconstruction that’s also compatible with BIM Authoring. The study focuses on various masonry vaulted systems. These types of structures are first examined using ideal models, which were perfectly discretised and set up by the user, and then employed as a starting point for validating the parameters of the RANSAC algorithm on point clouds acquired by laser scanners. These latter ones nevertheless have irregular geometries, making comprehension, analysis, and management far more challenging.

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace’s inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm’s later evaluation. The research’s findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites.