The conservation process is iterative and interactive. Periodic updates stratify data across disciplines and time. Still the transition from raw data to structured knowledge is often slowed by procedural gaps and tooling limitations, creating a semantic divide between abundant digital resources and truly intelligible data. This article proposes a methodological and operational approach for managing the continuity of the information flow within a digitalization process functional to a conservation strategy for the Historical Built Heritage. A graph-structured semantic knowledge base was developed and it is fed by data from heterogeneous sources (Building Information Modeling, reality-based annotation platforms and graph databases), organized according to an explicit conceptual model for representing the building’s diachronic evolution. Interaction and querying are mediated by a prototypical multidimensional visualization environment. The experimentation has proven to anticipate contextualization, to rationalize mapping, to harmonize heterogeneous resources, and to formalize knowledge for sharing and querying. Calabrian heritage, which is part of the region’s identity and subject to natural and anthropogenic risks, is the case of interest. Application scenarios are exemplified in the experiment on San Giovannello, Gerace (RC).

This article investigates retrieval-based localization in the context of heritage documentation, leveraging an extensive image dataset collected during the restoration of Notre-Dame de Paris. To address the challenges of image retrieval for localization, we first review state-of-the-art approaches, from all-in-one trained models to purely visual methods via 3D-based pipelines. Next, we evaluate various retrieval strategies, comparing purely visual approaches with spatial methods that prioritize spatial relationships between image locations. Finally, we present CIR4Loc, a retrieval framework based on the composed image retrieval (CIR) paradigm, introducing textual modifiers to refine retrieval towards configurations that enhance localization. By bridging the gap between visual and spatial retrieval, this approach ensures the selection of images that are both visually relevant and spatially distributed to improve pose estimation. We demonstrate the effectiveness of this proposal in a real-world heritage context, specifically the scientific site related to the restoration of Notre-Dame de Paris, emphasizing the necessity of retrieval strategies explicitly tailored for spatially aware localization.

Analyzing multimodal cultural heritage data, such as 2D images, 3D point clouds, and textual documentation, is essential for monument preservation but presents significant challenges due to data heterogeneity and the scarcity of annotated datasets. To address these limitations, we present a framework for Expert-Guided Annotation and Artificial Intelligence (AI) Models in Monument Analysis. Built upon the Notre-Dame de Paris digital ecosystem, our system enables domain experts to create high-quality, AI-ready datasets. Our framework introduces: (1) an enhanced, semi-automated annotation workflow capable of propagating expert annotations across modalities, increasing dataset volume by a factor of ten; (2) integrated 2D–3D visualization tools for intuitive annotation inspection and refinement; and (3) a standardized export system that produces datasets with rich semantic, geometric, and visual metadata in formats compatible with deep learning frameworks. We validate our system by generating annotations for degradation phenomena, architectural components, and decorative elements of Notre-Dame de Paris, and demonstrate their utility through integration with the Segment Anything Model (SAM) for semantic segmentation. Our approach bridges the gap between heritage expertise and computer vision, establishing a robust infrastructure for scalable, reproducible analysis and documentation.

The assessment of structural safety and a thorough understanding of buildings' structural behavior are critical to enhancing the resilience of the built environment. Cultural Heritage (CH) buildings present unique diagnosis challenges due to their diverse designs and construction techniques, often requiring attention during maintenance or disaster relief efforts. However, collaboration across CH and Architecture, Engineering, and Construction (AEC) fields is hindered by increasing information complexity and prolonged feedback loops. This paper introduces a methodological approach utilizing Knowledge Graph technologies to integrate structural diagnosis information and processes. The approach is applied to the diagnosis of the Notre-Dame de Paris buttressing system, demonstrated through a proof-of-concept knowledge system. By leveraging Knowledge Graph functionalities, insights are derived from the spatialization and provenance of mechanical phenomena, including observed or simulation-predicted cracks in mortar-bound masonry.

In Notre-Dame de Paris’ digital twin, the massive data is characterized by its variability in terms of production and documentation. The question of provenance appears as the missing link in digital heritage data and a fortiori in the provenance of knowledge. The problem can be formulated as follows: the heterogeneity of data means variability as multi-device, multitemporal, multiscalar, with spatial granularity, and multi-layered and semantic complexity. The objective of this article is to improve the quality and consistency of paradata and to bridge the practical gap between mass 3D digitization and mass data enrichment in the data lineage of cultural heritage digital collections. FAIR principles, provenance, and context are keys in the data management workflows. We propose an innovative solution to integrate provenance and context seamlessly into these workflows, enabling more cohesive and reliable data enrichment. In this article, we use both conceptual modeling and quick prototyping: we posit that existing conceptual models can be used as complementary modules to document the provenance and context of research activity metadata. We focus on three models, namely the W7, the PROV ontology, and the CIDOC CRM. These models express different aspects of data and knowledge provenance. The use case from Notre-Dame de Paris’ research demonstrates the validity of the proposed hybrid modular conceptual modeling to dynamically manage the Provenance Level of Detail in cultural heritage data.

Virtual reconstruction should move beyond merely presenting 3D models by documenting the scientific context and reasoning underlying the reconstruction process. For instance, the collapsed arch in the nave of Notre-Dame de Paris serves as a case study to make explicit the reconstruction argumentation encapsulated in relation to the spatial configuration of the arch and the voussoirs. The experiment is twofold: (1) setting up of the 3D dataset where the hypotheses are modeled as versions using logic programming, and (2) evaluating the scientific narrative of reconstruction through both a custom 2D-3D visualization and competency questions on the enriched 3D data. Formalization, reasoning, and visualization are combined to explore the nonlinear scientific hypotheses and narrative of the reconstruction. The results explicitly show both the factual information on the physical and digital objects, as well as the counterfactual propositions allowing the reasoning at play in the reconstruction. The hypotheses are visualized as counterfactual trajectories creating an open dynamic visualization that makes possible the spatialized querying of conflicting interpretations and embedded memory in place.

The 2019 fire of Notre-Dame de Paris was not only a significant event in its history, but also provided a unique platform for multidisciplinary studies and digital data analysis. At the initiative of the CNRS and the Ministry of Culture, a large-scale collaboration is currently underway to manage and analyse the extensive data generated in connection with the restoration of the cathedral. The role of digital data is crucial for understanding complex multidisciplinary studies, especially in the field of cultural heritage. A digital data working group, composed of thirty scientists from 12 laboratories, is focused on documenting and organising this collective experience to serve both the immediate needs of restoration and the longterm goals of cultural heritage research. Key challenges include data traceability, knowledge modelling, multidimensional analysis and the need to address gaps in semantic understanding, memory, data correlation and technology. To address these challenges, a novel method is proposed that combines the digital representation of material heritage with the evolving knowledge about it. By collaborating with the wider scholarly community involved in Notre Dame's scientific action, the aim is to introduce new methods in heritage research and bridge the gap between the humanities and digitally-driven scholarship. This initiative also intersects with two primary scientific vectors: the technological approach to data collection and the generation and analysis of semantically enriched datasets. (c) 2023 Published by Elsevier Masson SAS on behalf of Consiglio Nazionale delle Ricerche (CNR).

After the catastrophic fire at Notre Dame de Paris, a significant challenge was presented by the numerous lead-contaminated remnants. To address this, a detailed digitization strategy was devised and executed, tailored to the unique needs of this extensive and diverse corpus. This strategy involved the development and customization of both hardware and software tools, ensuring their effectiveness throughout the digitization process – from initial data acquisition to data dissemination.Central to our approach was the alignment of our methods with the distinct characteristics of each artifact, facilitating their effective preservation and future utility. Our strategy's adaptability was key, allowing us to incorporate advanced deep learning techniques into various aspects of our workflow. Notably, this included the implementation of the Segment Anything Model for automatic image segmentation, enhancing our image-based modeling capabilities. We also ventured into pioneering methods like 3D Gaussian Splatting and the exploration of radiance field methods for visualization.Moreover, the project has been mindful of data responsibility, aiming to make all digital data openly accessible beyond 2025. We have placed a strong emphasis on harmonizing and managing data, minimizing redundancies, and ensuring efficient storage, all while maintaining transparency about the limitations and errors in our methodologies. This holistic approach to digitization, balancing technological innovation with responsible data management, aims to preserve and make accessible the digital heritage of Notre Dame de Paris for future generations.

This paper conducts a comparative evaluation between Neural Radiance Fields (NeRF) and photogrammetry for 3D reconstruction in the cultural heritage domain. Focusing on three case studies, of which the Terpsichore statue serves as a pilot case, the research assesses the quality, consistency, and efficiency of both methods. The results indicate that, under conditions of reduced input data or lower resolution, NeRF outperforms photogrammetry in preserving completeness and material description for the same set of input images (with known camera poses). The study recommends NeRF for scenarios requiring extensive area mapping with limited images, particularly in emergency situations. Despite NeRF’s developmental stage compared to photogrammetry, the findings demonstrate higher potential for describing material characteristics and rendering homogeneous textures with enhanced visual fidelity and accuracy; however, NeRF seems more prone to noise effects. The paper advocates for the future integration of NeRF with photogrammetry to address respective limitations, offering more comprehensive representation for cultural heritage preservation tasks. Future developments include extending applications to planar surfaces and exploring NeRF in virtual and augmented reality, as well as studying NeRF evolution in line with emerging trends in semantic segmentation and in-the-wild scene reconstruction.

This article explores the design, development and deployment of a digital platform for scholarly work at Notre Dame Cathedral and demonstrates the transformative impact of digital technology on heritage disciplines. By merging technology and human expertise, the platform facilitates the creation, integration, sharing, and analysis of extensive scientific data on the multidisciplinary post-fire study of the cathedral. This multi-layered approach includes community building for collaborative efforts, digital tools tailored to different stakeholders, data structuring approaches for managing multidimensional features, and experience-based workflows for documenting, categorising and semantically enriching scientific and restoration data. The overall goal is to introduce an integrated solution for collaborative studies and to promote a digital memory of the collective initiative in accordance with the principles of FAIR for scientific heritage data. This initiative not only supports the research and restoration of Notre Dame, but also serves as a paradigm for future conservation and documentation efforts in the field of cultural heritage. (c) 2023 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights reserved.

After the fire that destroyed most of the Notre-Dame de Paris cathedral's roof and vaults, scientists gathered in an effort to help the restoration process of the cathedral. Several digital methods and heterogeneous data acquisitions were introduced in the process, including many images and annotations. Part of this data focuses on stone degradation phenomena, a crucial element when evaluating the damages caused by the fire and the state of the cathedral before the restoration started. In this paper, we present the first implementation of a dataset creation pipeline with the aim of training AI models to automatically detect and segment stone alteration patterns in images taken in the context of the restoration of Cultural Heritage buildings. Our resulting dataset will be improved in a near future with more data, while conforming with the ambition to provide our experts and researchers with reliable, structured data.