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Ai Applications in Cultural Heritage Preservation: Technologcal Advancements for the Conservation
(2023) Akyol, Gamze; Avcı, Ali Berkay
The protection of cultural heritage is very important for preserving the historical heritage of societies and for the continuation of cultural diversity. The emergence of artificial intelligence (AI) technologies has opened new horizons for innovative and efficient protection methods in this field. This study examines literature examples of applications of artificial intelligence in the preservation of cultural heritage, focusing on its impact on the digitization, documentation, analysis, restoration and preservation of cultural artifacts and historical sites. Artificial intelligence technologies are revolutionizing the digitization and documentation of cultural heritage assets. Automated scanning, 3D modeling, and virtual reality applications facilitate the creation of accurate digital copies, increasing accessibility for researchers, educators, and the public. In the field of conservation, artificial intelligence algorithms play a crucial role in identifying damage and formulating targeted restoration plans for deteriorated artifacts and structures. Using AI, image recognition and pattern detection, it assists experts in preserving sensitive artworks and archaeological items. AI also contributes to the protection of cultural heritage sites by addressing physical effects through environmental monitoring. AI-powered sensors and data analytics detect potential risks such as temperature changes, humidity fluctuations and air pollution, enabling timely response to reduce environmental impacts. Thanks to artificial intelligence, necessary precautions can be taken before existing or potential risks damage the heritage. In conclusion, AI applications in cultural heritage preservation represent a significant advance in the conservation and reintegration of collective heritage. By balancing technological innovation with ethical concerns, cultural heritage can be preserved in a sustainable and inclusive way for future generations.
Citation - WoS: 3
Citation - Scopus: 5
Deterioration of Pre-War and Rehabilitation of Post-War Urbanscapes Using Generative Adversarial Networks
(SAGE Publications, 2023) Çiçek, Selen; Turhan, Gözde Damla; Taşer, Aybüke
The urban built environment of contemporary cities confronts a constant risk of deterioration due to natural or artificial reasons. Especially political aggression and war conflicts have significant destructive effects on architectural and cultural heritage buildings. The post-war urbanscapes demonstrate the striking effects of the armed conflicts during the hot war encounters. However, the residues of the urbanscapes become the actual indicators of damage and loss. Since today we can make future predictions using a variety of machine learning algorithms, it is possible to represent hybrid projections of urban heterotopias. In this context, this research proposes to explore dystopian post-war projections for modern cities based on their architectural styles and demonstrate the utopian scenarios of rehabilitation possibilities for the damaged urban built environment of post-war cities by using generative adversarial network (GAN) algorithms. Two primary datasets containing the post-war and pre-war building facades have been given as the input data for the CycleGAN and pix2pix GAN models. Thus, two different image-to-image GAN models have been compared regarding their ability to produce legible building facade projections in architectural features. Besides, the machine learning process results have been discussed in terms of cities' utopian and dystopian future predictions, demonstrating the war conflicts' immense effects on the built environment. Moreover, the immediate consequence of the destructive aggression on tangible and intangible architectural heritage would become visible to inhabitants and policymakers when the AI-generated rehabilitation potentials have been exposed.

Architecture / Mimarlık

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