Advancements in 3D Heritage Data Aggregation and Enrichment in Europe: Implications for Designing the Jena Experimental Repository for the DFG 3D Viewer

Münster, Sander

doi:10.3390/app13179781

Cited by 7 publications

(5 citation statements)

References 68 publications

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“…Currently, most heritage data, AI models, and resources are held by companies outside Europe [50]. It is a major challenge to ensure the long-term maintenance, availability, and sustainability of AI tools, data, and platforms and foster open-source and open-data initiatives to not lose control and access to heritage and culture.…”

Section: Long-term Sustainabilitymentioning

confidence: 99%

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Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe

Münster,

Maiwald,

di Lenardo

et al. 2024

Heritage

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Artificial intelligence (AI) is a game changer in many fields, including cultural heritage. It supports the planning and preservation of heritage sites and cities, enables the creation of virtual experiences to enrich cultural tourism and engagement, supports research, and increases access and understanding of heritage objects. Despite some impressive examples, the full potential of AI for economic, social, and cultural change is not yet fully visible. Against this background, this article aims to (a) highlight the scope of AI in the field of cultural heritage and innovation, (b) highlight the state of the art of AI technologies for cultural heritage, (c) highlight challenges and opportunities, and (d) outline an agenda for AI, cultural heritage, and innovation.

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Section: Long-term Sustainabilitymentioning

confidence: 99%

“…Examples are metadata enrichment (e.g., [48][49][50]) and linking to open data sources (e.g., [33]).…”

mentioning

confidence: 99%

Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe

Münster,

Maiwald,

di Lenardo

et al. 2024

Heritage

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“…As an overlapping area, there are several automated 3D model creation processes that utilize extant imagery [77][78][79][80]. A major task is the provision of sufficient metadata to spatialize and temporalize this material [81].…”

Section: Data Retrievalmentioning

confidence: 99%

“…As another option for gathering 3D content, we implemented a low-end 3D digitization pipeline to document heritage with images taken with a smartphone (Figure 2). The goal was to document cultural heritage using images and 3D models from user-generated photos and to integrate the results in the DFG 3D-Viewer repository [81]. The web frontend and processing pipeline were in a beta state at the end of 2023, and had already contributed 3D sculptures to our 4D applications.…”

Section: Crowdsourced 3d Digitizationmentioning

confidence: 99%

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Münster,

Maiwald,

Bruschke

et al. 2024

Applied Sciences

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Numerous digital media repositories have been set up during recent decades, each containing plenty of data about historic cityscapes. In contrast, digital 3D reconstructions of no longer extant cityscapes have so far almost solely been performed for specific cases and via manual modelling techniques. Within the Jena4D research group, we are investigating and developing methods and technologies for transferring historical media and their contextual information into 4D models. The next step will be to automate this process and extend it to the world scale. Our team are working on different facets of that endeavor: retrieving images, text, and object data from public sources; crowdsourcing and collecting qualitative data from contests and pupil labs; processing historical plans and photographs to extract spatial data; location-based mobile visualization; and collection-browsing interfaces. This article is intended to highlight (1) current challenges, (2) the state of the art, (3) our approach to 4D modelling and visualization on the world scale, and (4) results from testing demo cases in Dresden, Jena, and Amsterdam.

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“…AI algorithms, including deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), could be applied for automated artifact recognition, textual analysis, and contextual understanding [95,98]. In other words, these technologies enable the efficient and accurate categorization, indexing, and annotation of digital repositories, facilitating easy access and reuse [99]. In addition, ML algorithms contribute to data mining, trend analysis, and predictive modeling, empowering heritage professionals to derive valuable insights and trends from extensive datasets [100].…”

Section: Cutting-edge Technologiesmentioning

confidence: 99%

Enhanced and Combined Representations in Extended Reality through Creative Industries

Anastasovitis,

Roumeliotis

2024

ASI

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The urgent need for research and study with nondestructive and noninvasive methods and the preservation of cultural heritage led to the development and application of methodologies for the multi-level digitization of cultural elements. Photogrammetry and three-dimensional scanning offer photorealistic and accurate digital representations, while X-rays and computed tomography reveal properties and characteristics of the internal and invisible structure of objects. However, the investigation of and access to these datasets are, in several cases, limited due to the increased computing resources and the special knowledge required for their processing and analysis. The evolution of immersive technologies and the creative industry of video games offers unique user experiences. Game engines are the ideal platform to host the development of easy-to-use applications that combine heterogeneous data while simultaneously integrating immersive and emerging technologies. This article seeks to shed light on how heterogeneous digital representations of 3D imaging and tomography can be harmoniously combined in a virtual space and, through simple interactions, provide holistic knowledge and enhanced experience to end users. This research builds on previous experience concerning the virtual museum for the Antikythera Mechanism and describes a conceptual framework for the design and development of an affordable and easy-to-use display tool for combined representations of heterogeneous datasets in the virtual space. Our solution was validated by 62 users who participated in tests and evaluations. The results show that the proposed methodology met its objectives. Apart from cultural heritage, the specific methodology could be easily extended and adapted for training purposes in a wide field of application, such as in education, health, engineering, industry, and more.

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Advancements in 3D Heritage Data Aggregation and Enrichment in Europe: Implications for Designing the Jena Experimental Repository for the DFG 3D Viewer

Cited by 7 publications

References 68 publications

Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe

Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Enhanced and Combined Representations in Extended Reality through Creative Industries

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