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• The Notre Dame fire highlighted the crucial role of digital data in heritage science, necessitating comprehensive multidisciplinary data interpretation. • State-of-the-art analysis identified gaps in semantics, memory, data correlation, and the balance between technology and human activity. • An initiative was launched to establish a digital ecosystem that represents both the tangible object and the evolving knowledge surrounding it. • The initiative integrates digital humanities, heritage science, and computer science, placing emphasis on an ecosystem tailored for human-led data collection and multi-dimensional data integration. • The Digital Data Working Group tackled challenges in digitisation and modelling, with a primary focus on studies of the wooden roof and collapsed vaults. 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 long-term 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. [ABSTRACT FROM AUTHOR]

The recording of cultural heritage objects' data and information is constructed throughout the registration of different media items, which function as an anchor of the original object in digital space. The article investigates the application of a theoretical framework for the organization of information related to the visual works, on the base of the identity of their single constituent elements. The framework is, then, used for the formalization of an ontology which is constructed as an extension of CIDOC-CRM. The result is tested with information gathered over the Asinou Church in Cyprus, expressed throughout an ontology for recording propositions over the iconographical attributes and the characteristic of images. The "Digital Heritage: Spotlight on Europe" column examines technological advances internal and external to cultural institutions. The digital shift changed radically how cultural heritage is made, disseminated, distributed, accessed, consumed, and monetized. One of the most important revolutions is that the user.fs role changed dramatically, shifting from passive observers to active participants and content producers with many new and exciting opportunities for engagement, creative use, and access. The strength of the column is its broad, international focus, and contributors are encouraged to explore issues and recent advances in digital heritage theories, methodologies, standards relevant to the European region, as well as the larger, global audience. Interested authors are invited to submit proposals and articles to the column editor atannamaria.tammaro@unipr.it. Please include "ILLR submission" in the subject line of the e-mail [ABSTRACT FROM PUBLISHER]

In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, multi-band and/or multi-epochs concurrently) implies several challenges in term of data provenance, data fusion and data analysis. Making the assumption that the current usability of multi-source 3D models could be more meaningful than millions of aggregated points, this work explores a "reduce to understand" approach to increase the interpretative value of multimodal point clouds. Starting from several years of accumulated digitizations on a single use-case, we define a method based on density estimation to compute a Multimodal Enhancement Fusion Index (MEFI) revealing the intricate modality layers behind the 3D coordinates. Seamlessly stored into point cloud attributes, MEFI is able to be expressed as a heat-map if the underlying data are rather isolated and sparse or redundant and dense. Beyond the colour-coded quantitative features, a semantic layer is added to provide qualitative information from the data sources. Based on a versatile descriptive metadata schema (MEMoS), the 3D model resulting from the data fusion could therefore be semantically enriched by incorporating all the information concerning its digitization history. A customized 3D viewer is presented to explore this enhanced multimodal representation as a starting point for further 3D-based investigations. [ABSTRACT FROM AUTHOR]

April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis. [ABSTRACT FROM AUTHOR]

Abstract: This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings. [Copyright &y& Elsevier]

Abstract: This article presents a set of theoretical reflections and technical demonstrations that constitute a new methodological base for the architectural surveying and representation using computer graphics techniques. The problem we treated relates to three distinct concerns: the surveying of architectural objects, the construction and the semantic enrichment of their geometrical models, and their handling for the extraction of dimensional information. A hybrid approach to 3D reconstruction is described. This new approach combines range-based modeling and image-based modeling techniques; it integrates the concept of architectural feature-based modeling. To develop this concept set up a first process of extraction and formalization of architectural knowledge based on the analysis of architectural treaties is carried on. Then, the identified features are used to produce a template shape library. Finally the problem of the overall model structure and organization is addressed. [Copyright &y& Elsevier]

Reflectance transformation imaging (RTI) is one of the most widely used techniques in order to digitize and analyze material appearance of a surface, finding a great level of utility and applicability in the field of cultural heritage as well as in industrial applications. To date, most of the methods used to process (model and relight) RTI data assume only one light direction for all pixels as well as a single light source-surface distance for the entire image, following the model of a very distant (far) light source. This assumption does not hold in practice. Indeed, the light sources commonly used in RTI acquisitions (spotlight/photo flash) induce to a non-uniform illumination of the surface. This is caused by the variation of incidence angles and per-point distances which directly affect the amount of light received by the surface. We propose a novel pixel-wise methodology for improving lighting based on illumination laws that allows one to correct both loss of energy due to the distance variation as well as the elevation angle. We show the efficiency of the proposed method on RTI acquisitions performed on cultural heritage objects and a manufactured surface. We show that our method corrects the effects of non-uniform illumination and leads to improve the relighting commonly associated with RTI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An innovative digital platform supports the multidisciplinary study of Notre Dame Cathedral's by centralizing and sharing scientific data. • A digital dataset that encapsulates collaborative heritage science efforts in line with FAIR principles. • An integrated process from on-site data collection to long-term semantic data preservation. • A dedicated suite of digital tools for data curation, analysis and semantic enrichment. • Detailed workflows of digital documentation and multidisciplinary data harmonization. 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. [ABSTRACT FROM AUTHOR]

Today, digital tools represent a privileged means of describing and understanding of cultural heritage. However, existing hybrid methods for semantic annotation on digital models are mostly manual and require considerable human intervention. This may cause data dispersion or data alteration issues, preventing the digital collection of multidisciplinary heritage information in collaborative valorization and conservation projects. Tackling this problem, this work presents three different methods for more automated 2D/3D data annotation, making the most of Machine Learning or Deep Learning and of the collaborative, web-based annotation platform Aïoli. The proposed approaches are designed to assist heritage experts in the hybrid annotation of architectural objects, e.g., to identify building components, degradation patterns, mapping of alterations, materials etc. and to share and access 2D/3D information via the web. The work aims at providing public and private stakeholders in charge of restoration and conservation activities with a set of more automated annotation tools, allowing to: i) preserve data in the transition to different representation types; ii) propagate analytically relevant information via digital models and/or images; iii) share heritage-related data on the web with an open source strategy. The validated workflows sketch the outlines of a dedicated and user-friendly toolbar for semi-automatic annotation, directly integrated into the Aïoli user interface, where automated annotation tools are readily available to the community of heritage professionals to properly support documentation and restoration activities. [ABSTRACT FROM AUTHOR]

We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage- or Historic-Building Information Modeling (H-BIM) reconstruction from laser scanning or photogrammetric surveys is a manual, time-consuming, overly subjective process, but the emergence of AI techniques, applied to the realm of existing architectural heritage, is offering new ways to interpret, process and elaborate raw digital surveying data, as point clouds. The proposed methodological approach for higher-level automation in Scan-to-BIM reconstruction is threaded as follows: (i) semantic segmentation via Random Forest and import of annotated data in 3D modeling environment, broken down class by class; (ii) reconstruction of template geometries of classes of architectural elements; (iii) propagation of template reconstructed geometries to all elements belonging to a typological class. Visual Programming Languages (VPLs) and reference to architectural treatises are leveraged for the Scan-to-BIM reconstruction. The approach is tested on several significant heritage sites in the Tuscan territory, including charterhouses and museums. The results suggest the replicability of the approach to other case studies, built in different periods, with different construction techniques or under different states of conservation. [ABSTRACT FROM AUTHOR]

This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The "Grand-Ducal Cloister" dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed. [ABSTRACT FROM AUTHOR]

Imaging techniques and Image Based-Modeling (IBM) practices in the field of Cultural Heritage (CH) studies are nowadays no longer used as one-shot applications but as various and complex scenarios involving multiple modalities; sensors, scales, spectral bands and temporalities utilized by various experts. Current use of Structure from Motion and photogrammetric methods necessitates some improvements in iterative registration to ease the growing complexity in the management of the scientific imaging applied on heritage assets. In this context, the co-registration of photo-documentation among other imaging resources is a key step in order to move towards data fusion and collaborative semantic enrichment scenarios. This paper presents the recent development of a Totally Automated Co-registration and Orientation library (TACO) based on the interoperability of open-source solutions to conduct photogrammetric-based registration. The proposed methodology addresses and solves some gaps in term of robustness and versatility in the field of incremental and global orientation of image-sets dedicated to CH practices. [ABSTRACT FROM AUTHOR]

The conservation and restoration of historical monuments require a diagnostic analysis carried out by a multidisciplinary team. The results of the diagnosis include data produced by different techniques and protocols, which are used by conservation scientists to assess the built heritage. Nowadays, together with the aforementioned data, a great deal of heterogeneous information is also available, including descriptive and contextual information, as well as 2D/3D geometrical restitution of the studied object. However, the integration of these diverse data into a unique information model capable of fully describing the building conservation state, as well as integrating future data, is still an open issue within the Cultural Heritage community. It is of paramount importance to correlate these data and spatialize them in order to provide scientists in charge of our heritage with a practical and easy means to explore the information used during their assessment, as well as a way to record their scientific observation and share them within their community of practice. In order to resolve this issue, we developed a correlation pipeline for the integration of the semantic, spatial and morphological dimension of a built heritage. The pipeline uses an ontological model for recording and integrating multidisciplinary observations of the conservation state into structural data spatialized into a semantic-aware 3D representation. The pipeline was successfully tested on the Saint Maurice church of Caromb in the south of France, integrating into a unique spatial representation information about material and alteration phenomena, providing users with a means to correlate, and more importantly retrieve several types of information. [ABSTRACT FROM AUTHOR]

The innovative automated 3D modeling procedure presented here was used to reconstruct a Cultural Heritage (CH) object by means of an unmanned aerial vehicle. Using a motion capture system, a small low-cost quadrotor equipped with a miniature low-resolution Raspberry Pi camera module was accurately controlled in the closed loop mode and made to follow a trajectory around the artifact. A two-stage process ensured the accuracy of the 3D reconstruction process. The images taken during the first circular trajectory were used to draw the artifact’s shape. The second trajectory was smartly and autonomously adjusted to match the artifact’s shape, then it provides new pictures taken close to the artifact and, thus, greatly improves the final 3D reconstruction in terms of the completeness, accuracy and quickness, in particular where the artifact’s shape is complex. The results obtained here using close-range photogrammetric methods show that the process of automated 3D model reconstruction based on a robotized quadrotor using a motion capture system is a realistic approach, which could provide a suitable new digital conservation tool in the cultural heritage field. [ABSTRACT FROM AUTHOR]

Abstract: Stone conservation at Chambord Castle and monitoring of its changes over time is actually an urgent necessity in order to preserve and enhance this historic and tourist site. Its porous stones suffer from degradation problems depending on physical and chemical parameters that are the origin of frequent restorations. The goal of this research is to supply authorities of the castle with a tool for scientific monitoring and decision support, based on the acquisition of alteration mapping and on the estimation of degradation patterns, in order to permit rational programming operations of restorations. This paper proposes an approach for digital documentation about the conservation state of buildings. The approach is based on the connection between a structured 3D model and structured 2D mapping data. In particular, in the NUBES web platform, a specific interface has been implemented, permitting the displaying and cross-reference of 2D mapping data on the 3D model in real time, by means of structured 2D layer-like annotations concerning stone degradation, dating, and material. [Copyright &y& Elsevier]

Reflectance transformation imaging (RTI) is a multilight-based imaging technique that can provide relevant information on both local microgeometry and visual appearance of a studied surface. The pixelwise angular reflectance is modeled to allow the relighting of the surface appearance under any arbitrary light direction. The primary methods used to model this local reflectance in each pixel are polynomial texture mapping, hemispherical harmonics, and, more recently, discrete modal decomposition. For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it is not always possible to satisfy this condition in practice. As a result of this nonuniform distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we propose a methodology consisting of the estimation of the spatial distribution of the lighting directions used during RTI acquisitions based on a local density estimation. These local density values are used afterward to weight the least square regression and thus correct the contributions of each image to the RTI acquisition. This methodology is applied on three surfaces with visual singularities, which present different reflectance responses. From the presented results, it can be concluded that it is necessary to take into account this nonuniformity in order not to alter the quality of reconstruction/relighting from RTI data and the subsequent inspection task. [ABSTRACT FROM AUTHOR]