ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models
Journal:
arXiv
Published Date:
Jun 9, 2025
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.
