Building Architectural Styles Classification Using Convolutional Neural Networks Models

An intelligent model for classifying architectural styles is presented in this chapter. Traditional machine vision methods face difficulties in image classification of architectural styles, particularly in the feature extraction phase where many visual features need to be extracted, refined, and optimized. This step is typically conducted manually in traditional Machine Learning (ML) models. However, with the emergence of Deep Learning (DL), these challenging operations can now be automated. In this study, a DL-based machine vision model was developed to assess the effectiveness of DL in solving the classification problem. The proposed model was trained and validated on a publicly available dataset containing over 5000 images of 8 different architectural styles. The results indicate that the proposed model achieved a classification accuracy of 95.44%, demonstrating its reliability.