Title : Understanding architecture age and style through deep learning

The paper "Understanding Architecture Age and Style through Deep Learning" presents an innovative approach for analyzing architecture using deep learning and computer vision. It focuses on identifying and understanding architectural styles and epochs through urban street-level imagery. Traditionally, analyzing architectural styles and their evolution requires specialized knowledge, fieldwork, and complex manual processes. However, recent advancements in deep learning and image processing have enabled automating these processes with high accuracy.
The proposed framework consists of two main stages:
1. Deep Learning the Architecture: In this stage, a deep convolutional neural network (DCNN) model is designed to automatically learn about the age characteristics and styles of building façades from street-level images. The model learns architectural features such as materials, decorative details, and forms, each reflecting a specific architectural period or style. This allows the model to classify buildings based on their age and stylistic characteristics.
2. Deep Interpreting the Architecture: After training the model, three components are introduced to interpret the architectural epochs and styles. These components help in understanding how architecture evolves both spatially and temporally. By comparing different styles and ages of buildings, the model analyzes the historical trends in urban environments.
Experimental Data:
The study utilizes datasets from the cities of Amsterdam and Stockholm, compiling information about building ages and styles. These datasets enable researchers to explore the evolution of architectural elements and the relationship between the age and style of buildings in a spatial and temporal context. The goal is to trace the changes in architectural trends across different urban areas over time.
Conclusion:
The paper demonstrates that deep learning and street-level imagery, as publicly available data sources, can be powerful tools for architectural analysis. This approach allows architects and researchers to study architectural styles and their historical transformations with greater accuracy and efficiency, without the need for extensive fieldwork. By using these methods, a comprehensive and multidimensional view of the architectural history of cities can be created, exploring how styles evolve in urban and spatial contexts.

This research highlights the potential for deep learning and digital tools to revolutionize how we understand the history and evolution of architectural styles, particularly in historic cities where detailed analysis is often time-consuming and resource-intensive.