A 3D-based Web Platform as an innovative tool to support the Management and Visualization of Multi-source Diagnostic Data

In the field of cultural heritage conservation, handling diagnostic data is a complex task which requires the integration of heterogeneous data collected through multiple analytical/surveying techniques and processed with tools that are frequently not interoperable. For this reason, in recent years, research has placed growing emphasis on developing digital tools and platforms that enable shared and integrated data management. Within the CHANGES (Cultural Heritage Active Innovation for Next-Generation Sustainable Society) project, a methodological workflow was developed to address these issues through a unified digital approach combining data acquisition, structuring, visualization, and sharing. The core innovation of the workflow lies in a 3D-based web platform, developed by exploiting open source technologies. This system is built on the 3DHOP framework, can support high-resolution 3D models (such as those obtained from digital metric acquisition systems, like laser scanning and multi-image photogrammetry), and is capable of integrating and visualizing diverse datasets within a unified 3D digital environment. It allows users to access diagnostic data through annotated 3D models, enabling the exploration of specific areas of interest and the retrieval of associated analytical information. This approach strengthens spatial understanding of material properties and degradation patterns while fostering multidisciplinary collaboration by linking physical evidence to analytical results and historical context. The platform relies on a JSON-driven data management system, providing a flexible and extensible framework for both data entry and visualization. Each diagnostic analysis is associated with a JSON template, defined in close collaboration with experts in the specific analysis domains, and able to synthetize the relevant information characterizing each analysis. This template consists of modular fields (text, images, tables, downloadable resources) which can be customized to adapt the data structure to the peculiar needs of the different analysis. Beyond visualization, the platform supports multiple sharing options, which, together with the remote access possibilities, enable collaborative analysis among users from different disciplines. Its modular architecture also allows for potential integration with external repositories, making the platform a centralized hub for cultural heritage scientific data. The fusion of multi-source and multi-sensor data within the platform exemplifies a data-driven approach to heritage diagnostics. By combining analytical results with spatial data in an interactive 3D environment, it enhances understanding of degradation mechanisms and supports the development of preventive conservation strategies.