Enhancing Heritage fruition through 3D semantic modelling and digital tools: the INCEPTION project

Maietti, Federica; Giulio, Roberto Di; Piaia, Emanuele; Medici, Marco; Ferrari, Federico

doi:10.1088/1757-899x/364/1/012089

Cited by 30 publications

(17 citation statements)

References 4 publications

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“…Another task pursued through the digitalization of heritage archives is enabling multimedia applications, for visualization or analysis purposes (Auer et al, 2014;Scianna et al, 2013). Among current works presenting advancements in this direction, the Arches project and the INCEPTION project can be cited (Arches project, 2018; INCEPTION project, 2018; Maietti et al, 2018;Myers et al, 2016). Both of these are dealing with standard compliant collections, with the aim to make an interoperable information available through multiple services like websites, digital maps, apps… Similar issues are faced by the team from the Università di Padova which is working at the present project.…”

Section: Digital Archivesmentioning

confidence: 86%

Towards Deep Learning for Architecture: A Monument Recognition Mobile App

Palma

2019

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

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<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.</p>

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Section: Digital Archivesmentioning

confidence: 86%

Towards Deep Learning for Architecture: A Monument Recognition Mobile App

Palma

2019

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

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“…In recent years there has been an ever-increasing interest in advancing 3D modelling technologies that can create a virtual replica of CH objects, display 3D CH models in web browsers, and in the enrichment of 3D CH models with related contextual information using annotation concepts and the semantic web [60,61]. In the CH domain, there have been numerous large-scale projects aligned with the above-indicated points such as INCEPTION (Inclusive CH in Europe through 3D Semantic Modelling) [62], 3D-COFORM [63] and others. Furthermore, many CH institutions now have a repository of 3D models that are available to online users for exploration [6,64].…”

Section: D Geospatial Semantic Webmentioning

confidence: 99%

A Survey of Geospatial Semantic Web for Cultural Heritage

2019

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The amount of digital cultural heritage data produced by cultural heritage institutions is growing rapidly. Digital cultural heritage repositories have therefore become an efficient and effective way to disseminate and exploit digital cultural heritage data. However, many digital cultural heritage repositories worldwide share technical challenges such as data integration and interoperability among national and regional digital cultural heritage repositories. The result is dispersed and poorly-linked cultured heritage data, backed by non-standardized search interfaces, which thwart users’ attempts to contextualize information from distributed repositories. A recently introduced geospatial semantic web is being adopted by a great many new and existing digital cultural heritage repositories to overcome these challenges. However, no one has yet conducted a conceptual survey of the geospatial semantic web concepts for a cultural heritage audience. A conceptual survey of these concepts pertinent to the cultural heritage field is, therefore, needed. Such a survey equips cultural heritage professionals and practitioners with an overview of all the necessary tools, and free and open source semantic web and geospatial semantic web platforms that can be used to implement geospatial semantic web-based cultural heritage repositories. Hence, this article surveys the state-of-the-art geospatial semantic web concepts, which are pertinent to the cultural heritage field. It then proposes a framework to turn geospatial cultural heritage data into machine-readable and processable resource description framework (RDF) data to use in the geospatial semantic web, with a case study to demonstrate its applicability. Furthermore, it outlines key free and open source semantic web and geospatial semantic platforms for cultural heritage institutions. In addition, it examines leading cultural heritage projects employing the geospatial semantic web. Finally, the article discusses attributes of the geospatial semantic web that require more attention, that can result in generating new ideas and research questions for both the geospatial semantic web and cultural heritage fields.

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“…From the left: integrated data capturing through the data acquisition protocol, advancement in 3D models processing in BIM environment and model sharing. 3D modelling approach, improving an inclusive understanding of European cultural identity and diversity [13].…”

Section: Integrated 3d Survey and Holistic Documentationmentioning

confidence: 99%