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_id	ecaade2024_11
id	ecaade2024_11
authors	Yilmaz, Emirkan Burak; Tan Bayram, Funda; Balcan, Cem; Arslantürk, Esra; Arslan Ercan, ªeyda; Akgül, Yusuf Sinan
year	2024
title	Viewing History through the Lens of Artificial Intelligence: Classification of late Ottoman and early Republican period buildings in Türkiye with Convolutional Neural Network (CNN)
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 565–574
doi	https://doi.org/10.52842/conf.ecaade.2024.1.565
summary	This study employs Convolutional Neural Networks (CNNs) to classify late Ottoman and early Republican period buildings in Türkiye, offering a unique lens through artificial intelligence (AI) to examine architectural styles. By training on a specially curated dataset, including images of 16 architects' works, the study achieves accuracy rates of 84.65% for a limited architect dataset and 74.08% for the full architect dataset. EfficientNet emerges as the optimal architecture, surpassing Baseline, VGG, and ResNet models. Through t-Distributed Stochastic Neighbor Embedding (t-SNE), the model visualizes relationships among architects' styles. This research not only provides a new perspective on Turkey's architectural heritage but also establishes a platform for future AI-driven architectural analyses and design paradigms.
keywords	Convolutional Neural Networks, Architectural Style Classification, Late Ottoman Period, Early Turkish Republican Period
series	eCAADe
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last changed	2024/11/17 22:05

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