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

2025-06-09

AI Summary

Traditional architectural cultural research has long relied on expert subjectivity and literature reviews, suffering from regional bias, poor reproducibility, and inadequate characterization of visual features. To address these limitations, this paper proposes ArchiLense—a novel vision-language co-analytic framework for architecture. It introduces ArchDiffBench, the first high-quality, multi-source benchmark dataset for architectural style analysis. ArchiLense integrates state-of-the-art vision-language models (e.g., CLIP, Qwen-VL) with backbone architectures (e.g., ViT, ResNet) to enable automated architectural image recognition, cross-temporal-spatial style comparison, fine-grained classification, and generation of interpretable stylistic descriptions. Experimental results demonstrate 92.4% agreement between model-predicted and expert-annotated styles, and an 84.5% classification accuracy—substantially enhancing objectivity, quantification, and scalability in architectural style analysis.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Architectural cultures across regions are characterized by stylistic diversity, shaped by historical, social, and technological contexts in addition to geograph-ical 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 ex-planatory 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 ad-vanced computer vision techniques, deep learning, and machine learning algo-rithms, ArchiLense enables automatic recognition, comparison, and precise classi-fication of architectural imagery, producing descriptive language outputs that ar-ticulate stylistic differences. (3) Extensive evaluations show that ArchiLense achieves strong performance in architectural style recognition, with a 92.4% con-sistency rate with expert annotations and 84.5% classification accuracy, effec-tively 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.

Problem

• Overcoming subjective expert biases in architectural style analysis
• Automating recognition and classification of diverse architectural styles
• Providing objective quantitative analysis across regions and periods

Innovation

• Professional architectural style dataset ArchDiffBench
• Vision-Language Models framework ArchiLense
• Automatic recognition and classification of architecture