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Yazar "Oke, Adeoti Gerard" seçeneğine göre listele

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    Use of Augmented Reality in the Preservation of Architectural Heritage: Case of the Aqueduct Kuru Kopru (Kayseri, Turkey)
    (Selcuk University Faculty of Architecture, 2021) Oke, Adeoti Gerard; Kozlu, Hale Hayriye; Çoruh, Levent
    Purpose The architectural preservation embeds all the activities dealing with the sustainability of the built heritage. The representation and diffusion of heritage take a core place in that process. This study aims at the diffusion of the Aqueduct Kuru Kopru towards a broad public audience, establishment of the awareness that Augmented Reality (AR) methods can be used for the evaluation and presentation of the semi-collapsed structures and serving as a model for the representation and diffusion of virtual reconstructions of Architectural Heritage (AH). Design/Methodology/Approach This work refers to restoration and heritage visualization charters, combines Computer-Aided Design (CAD), photogrammetry survey method, and advanced texturing techniques to acquire and model virtual reconstructions of the Aqueduct Kuru Kopru from the Roman-Byzantine period to the year 2017. The paper also describes the frameworks for the implementation of a non-immersive and an immersive AR application to visualize modelled reconstructions. The method used consists of three main stages: modelling of the virtual reconstructions, texturing of models, and development of AR applications. Findings This study establishes AR and cultural heritage digitization techniques as efficient tools to represent and diffuse the Aqueduct Kuru Kopru. It engages a broad public audience in better comprehension and assessment of the aqueduct. Research Limitations/Implications The fact that the research is based on a single case study is a limitation for its generalisation. However, the case examined provides a basis for future work that may validate its findings in different contexts. Originality/Value The paper proposes the rediscovering of long-abandoned aqueduct Kuru Kopru by learning its spatio-temporal evolution through AR. The developed methodology can be easily implemented and puts forward the use of low-cost materials.

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