Advanced Data Environment in Current Cultural Heritage 3D Digitization Practices

A special issue of Heritage (ISSN 2571-9408).

Deadline for manuscript submissions: 17 January 2025 | Viewed by 927

Special Issue Editors


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Guest Editor
Dipartimento di Architettura, Alma Mater Studiorum Università di Bologna, Viale Risorgimento, 2-40136 Bologna, Italy
Interests: cultural heritage digitization; real-time rendering; 3D modeling; architecture; archeology; reverse modeling; 3D scanning; photogrammetry; virtual reality
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Guest Editor
UPR CNRS 2002 MAP, Marseille, France
Interests: cultural heritage digitization; 3D modeling; architecture; 3D scanning; photogrammetry; image-based modeling; multi-light image collection; multimodal imaging

Special Issue Information

Dear Colleagues,

Three-dimensional scanning in the mid-1990s and later digital photogrammetry appeared as techniques to digitally reproduce the shape and appearance of physical objects. With this aim, they have also been introduced in the field of cultural heritage (CH), with an increasing scope of techniques to capture geometry (IMMS and SLAM) and appearance features (MLIC and RTI). However, this purpose soon turned out to be very limited compared to the cognitive, operational, and communicative needs of the existing heritage.

CH studies are at the crossroads of many disciplines, including heritage sciences and data sciences. For the past few decades, a large number of experts, projects, and experiences have contributed to building this field of study from digitized or digitally born resources. The digital heritage scientific community is currently growing and evolving, generating at the same time a mass of data composed of images, 3D models, maps, databases, web components, metadata, and so on. The way to handle and manage the data along its lifecycle differs from experts, groups, and institutions at all scales and across any borders; those protocols are mostly intangible and not often documented. All of us know that data management is a multifaceted activity occurring all over the operating chain. It is also dependent on the data environment, understood as the collection of computer systems and associated infrastructure, to store, enrich, share, and expose raw or curated data. The need for new or strengthened data management is motivated by many factors: the sophistication and automation level of digitization devices and processes, the increasing computational power, and the miniaturization of sensors and electronic chips. All these points have augmented our capacity to collect, process, and store data by leaving an empty space on how to structure, sort, and organize the research data that is nowadays incoming in unprecedented volume. Another factor contributing to this overgrowing mass of data concerns the emphasizing of semantic aspects (i.e., the cognitive, informative, or descriptive added value of digital assets and resources). Furthermore, the rising challenge of interoperability implies expanding digitization results toward new types of outputs that are recognizable, reproducible, and capable of bringing new knowledge in order to enable more effective conservation processes and easier communication of asset consistency and value.

This Special Issue focuses on the methods to improve data management throughout the workflows leading to the creation of a digital heritage asset or corpus, exploiting 2.5D- and 3D-based digital capture techniques. The scope is widely open to obtain an overview of current practices at different scales, for example:

—  Data management dedicated to massive digitization or managing large and complex CH sites or collections of objects.

— Optimization of data management in processing pipelines.

— Low-level or top-level semantic enrichment by user-driven and/or computer-driven approaches.

— Reinforcing data provenance, traceability, and lineage from raw data acquisition toward interoperable web-semantic scenarios.

— Integration of real-based modeling CH projects in an advanced digital ecosystem (data warehouse, data lake, open-access repository, etc.).

Original research, review articles, case studies, and research or papers focusing on the key role of data management involving digitization frameworks or digitized CH assets are accepted. Papers focusing on related issues of sustainability, digital sobriety, and cost-effective technologies to reduce the data weight in the energy crisis are also welcome. 

This Special Issue will welcome manuscripts that link the following themes:

  • 3D data capture for manageable heritage data;
  • Range and image-based 3D reconstruction for digital documentation efficiency;
  • Multimodality and data integration;
  • Data science;
  • Metadata and paradata;
  • 3D-based information systems;
  • CH data organization;
  • Semantics for CH data organization.

We look forward to receiving your submissions.

Prof. Dr. Marco Gaiani
Dr. Anthony Pamart
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Heritage is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cultural heritage
  • data management
  • digital heritage
  • 3D scanning for CH data management
  • photogrammetry for CH data management
  • CH 3D-based information systems
  • web-based information systems
  • semantics

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Published Papers (1 paper)

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Research

28 pages, 16840 KiB  
Article
Working in Tandem to Uncover 3D Artefact Distribution in Archaeological Excavations: Mathematical Interpretation through Positional and Relational Methods
by Miguel Ángel Dilena
Heritage 2024, 7(8), 4472-4499; https://doi.org/10.3390/heritage7080211 - 18 Aug 2024
Viewed by 610
Abstract
In recent years, the most advanced pioneering techniques in the computing field have found application in assorted areas. Deep learning approaches, including artificial neural networks (ANNs), have become popular thanks to their ability to draw inferences from intricate and seemingly unconnected datasets. Additionally, [...] Read more.
In recent years, the most advanced pioneering techniques in the computing field have found application in assorted areas. Deep learning approaches, including artificial neural networks (ANNs), have become popular thanks to their ability to draw inferences from intricate and seemingly unconnected datasets. Additionally, 3D clustering techniques manage to associate groups of elements by identifying the specific inherent structures exhibited by such objects based on similarity measures. Generally, the characteristics of archaeological information gathered after extraction operations align with the previously mentioned challenges. Hence, an excavation could be an opportunity to use these prior innovative computing approaches. Our objective is to integrate software techniques to organise recovered artefacts and derive logical conclusions from their spatial location and the correlation between tangible attributes. These results can statistically improve our approach to investigations and provide a mathematical interpretation of archaeological excavations. Full article
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