ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models

Architectural cultures across regions are characterized by stylistic diversity, shaped by historical, social, and technological contexts in addition to geographical conditions. Understanding architectural styles requires the ability to describe and analyze the stylistic features of different architects from various regions through visual observations of architectural imagery. However, traditional studies of architectural culture have largely relied on subjective expert interpretations and historical literature reviews, often suffering from regional biases and limited explanatory scope. To address these challenges, this study proposes three core contributions: (1) We construct a professional architectural style dataset named ArchDiffBench, which comprises 1,765 high-quality architectural images and their corresponding style annotations, collected from different regions and historical periods. (2) We propose ArchiLense, an analytical framework grounded in Vision-Language Models and constructed using the ArchDiffBench dataset. By integrating advanced computer vision techniques, deep learning, and machine learning algorithms, ArchiLense enables automatic recognition, comparison, and precise classification of architectural imagery, producing descriptive language outputs that articulate stylistic differences. (3) Extensive evaluations show that ArchiLense achieves strong performance in architectural style recognition, with a 92.4% consistency rate with expert annotations and 84.5% classification accuracy, effectively capturing stylistic distinctions across images. The proposed approach transcends the subjectivity inherent in traditional analyses and offers a more objective and accurate perspective for comparative studies of architectural culture.