Title : Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks

1. Objectives
The primary objectives of this article are:
- Enhancing architectural style identification: Utilizing deep learning techniques to improve the identification and classification of architectural styles from building images.
- Automating the classification process: Enhancing accuracy by automating the classification of architectural styles, which traditionally relied on manual analysis and expert knowledge.

2. Strategies and Methods
The strategies and methods employed in this study include:
- Convolutional Neural Networks (CNNs): Using CNNs to process and analyze visual features of building images for style identification.
- Data preprocessing: Standardizing images for size, resolution, and lighting before feeding them into the neural network.
- Dataset splitting: Dividing the dataset into training and testing sets to evaluate the model’s performance.

3. Applications
The applications of deep learning in architecture include:
- Architectural style identification: CNN models used to classify buildings based on their architectural style.
- Architectural history analysis: Analyzing the evolution of architectural styles over time.
- Design enhancement: Assisting architects by predicting and incorporating features from different architectural styles into their designs.

4. Models and Algorithms
The following models and algorithms were used:
- Basic CNN: For feature extraction from building images and recognizing architectural patterns.
- Advanced CNN models (e.g., ResNet, VGG): Used to improve accuracy and speed of the model.
- Optimization algorithms: Using Stochastic Gradient Descent (SGD) to train the models.

5. Results
The results indicate that:
- High accuracy: CNN models are highly accurate in identifying architectural styles from images.
- Pattern recognition: CNNs can identify complex architectural patterns that may be missed by humans.
- Superior performance: The deep learning approach outperforms traditional methods of architectural style classification.

6. Challenges and Limitations
Challenges and limitations include:
- Data quality: The need for high-quality and diverse datasets for training the models.
- Computational resources: Complex models require significant computational power.
- Cultural and regional variations: Difficulty in accurately classifying styles due to cultural and geographical differences.

7. Conclusion
In conclusion, deep learning models, specifically CNNs, prove to be powerful tools for architectural style classification. The results demonstrate that these models offer a highly accurate and efficient alternative to traditional methods.

8. Future Work
Future work may focus on:
- Increasing dataset diversity: Collecting more diverse datasets to train better models.
- Combining with other technologies: Integrating deep learning models with other technologies such as Natural Language Processing for better architectural feature analysis.
- Improving model efficiency: Developing models that require fewer computational resources while maintaining high accuracy.