Arcangelo Priore

Historical water management infrastructures, often comprising underground environments, represent a significant example of the interplay between built heritage and the natural substrate. This study proposes an interdisciplinary, integrated and multi-scalar investigative methodology for such structures. Through the analysis of the case study of Palombaro Purgatoro Vecchio, a large historical public water cistern located in Matera in Italy, this paper presents a rigorous methodology replicable in different contexts. Bibliographic and archival research establish the knowledge base regarding the structure’s historical evolution; territorial and hydromorphic analyses, supported by GIS, highlight the dynamics of the surrounding watersheds. Meanwhile, a digital survey integrating SLAM and photogrammetry provides geometric-dimensional data, serving as the foundation for analysing construction techniques and materials. The selection of accessible and manageable technologies promotes a practical, replicable investigative methodology aimed at the protection, comprehension, enhancement and dissemination of water UBH.

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.

This study examines change detection techniques in dense point clouds for the purpose of cultural heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring of changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of cultural heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.

This study examines change detection techniques in dense point clouds for the purpose of Cultural Heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via advanced laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of Cultural Heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.