The Production of 3D Digital Archives and the Methodologies for Digitally Supporting Research in Architectural and Urban Cultural Heritage

Apollonio, Fabrizio Ivan

doi:10.1007/978-3-319-76992-9_9

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…Despite the increasing use of new measuring platforms and computer modeling techniques, the implementation of 3D building models in smart tourism services is still limited, focusing more on the results of scientific projects rather than on the implementation of the new ones. In the literature on the subject, few studies concentrate on the need for centralized 3D cultural heritage archives [ 23 , 36 ]. Most of them focus on peer-reviewed 3D models and a methodological approach useful for producing 3D digital content of cultural heritage objects in the context of digital archives.…”

Section: Methodsmentioning

confidence: 99%

The Use of Low-Cost Unmanned Aerial Vehicles in the Process of Building Models for Cultural Tourism, 3D Web and Augmented/Mixed Reality Applications

Templin

Popielarczyk

2020

Sensors

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Unmanned Aerial Systems (UAS) are widely used in low-cost photogrammetry. Even small Unmanned Aerial Vehicles (UAV) can deliver valuable data for the inventory of inaccessible and dangerous areas or objects. The acquisition of data for 3D object modeling is a complicated, time-consuming, and cost-intensive process. It requires the use of expensive equipment and often manual work as well as professional software. These are major barriers limiting the development of modern tourist platforms that promote local attractions. Information technologies offer new opportunities for the development of the services market, including the development of smart tourism services, as an integral part of the smart city concept. 3D models are an important element of this process as they form the basis for the use of new visualization technologies, such as Virtual, Mixed, and Augmented Reality (VR/MR/AR). 3D modeling provides a new opportunity to use AR/MR technology to present information about objects, virtual tours of the historic buildings, and their promotion. It also creates an opportunity to preserve the architectural heritage and preventive maintenance of buildings. Despite the increasing use of new measuring platforms and computer modeling techniques, the implementation of 3D building models in smart tourism services is still limited, focusing more on the results of scientific projects rather than on the implementation of the new ones. The paper presents an universal methodology for the inventory of historical buildings using low-cost UAVs. It describes the most important aspects related to the process of planning UAV measurement missions and photogrammetric data acquisition. The construction of 3D models and the possibilities of their further use to build smart tourism services based on Web/AR/MR/VR technology was also presented.

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Section: Methodsmentioning

confidence: 99%

The Use of Low-Cost Unmanned Aerial Vehicles in the Process of Building Models for Cultural Tourism, 3D Web and Augmented/Mixed Reality Applications

Templin

Popielarczyk

2020

Sensors

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“…As sets of data virtual reconstructions may contain single pieces of information such as construction data, source extracts, surveys and documentation embedded in a multidimensional context. The semantic virtual environment platforms, able to visually present space enriched by a range of meta-information, act as medium/metaphors for the spatially organized 'interface', which refers to an essential scientific framework (Hoppe and Breitling, 2016), and becomes the engine for dissemination of different and customized level of knowledge (Apollonio, 2018). According to theoretical humanities approaches to knowledge as knowing, observer dependent, emergent, and process-driven rather than entitydefined, even though web-based ICT systems can offer increasingly updated tools for the cultural heritage management, we need to adopt a transparent reconstruction workflow, and to define standardized methodology of source or reality-based 3D reconstruction of tangible cultural heritage, able to ensure:  3D modeling qualified by readable quality/properties;  a proper semantic structure of the 3D digital model;  a retrievable knowledge reconstruction and formalization process (Apollonio, 2018);  the interoperability of data sets by referring to recognized standard reference ontologies.…”

Section: Challenges: Formalizing Semantic Knowledge and New Forms Of mentioning

confidence: 99%

Digital Cultural Heritage Meets Digital Humanities

Münster

Apollonio

Bell³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Digital Cultural Heritage and Digital Humanities are, historically seen, in focus of different communities as well as approaching different research topics and - from an organizational point of view - departments. However, are they that different? The idea of this joint article involving digital humanists and heritage researchers is to examine communities, concepts and research applications as well as shared challenges. Beyond a collection of problem-centred essays this is intended to initiate a fruitful discussion about commonalities and differences between both scholarly fields as well as to assess to which extent they are two sides of the same medal.</p>

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“…The available information produced in the framework of architectural heritage studies can today be referred directly to a three-dimensional (3D) representation of the study object. This allows several tasks to be accomplished, including the mapping of materials and degradation states [1][2][3][4], the depiction of frescoed surfaces [5], morphological and historical analyses [6,7] and the detection of areas needing recovery measures [8,9].…”

Section: Introductionmentioning

confidence: 99%

From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

et al. 2021

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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.

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The Production of 3D Digital Archives and the Methodologies for Digitally Supporting Research in Architectural and Urban Cultural Heritage

Cited by 10 publications

References 28 publications

The Use of Low-Cost Unmanned Aerial Vehicles in the Process of Building Models for Cultural Tourism, 3D Web and Augmented/Mixed Reality Applications

The Use of Low-Cost Unmanned Aerial Vehicles in the Process of Building Models for Cultural Tourism, 3D Web and Augmented/Mixed Reality Applications

Digital Cultural Heritage Meets Digital Humanities

From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

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