Research Topics on ASM (Architectural Style Metrics)
Architectural design analysis has traditionally relied on subjective interpretations, which often lead to inconsistencies and inefficiencies. To address this challenge, this study introduces Architectural Style Metrics (ASM), a methodology that leverages CLIP (Contrastive Language-Image Pre-training) to quantitatively assess the visual characteristics of architectural designs. By translating qualitative visual elements into measurable metrics, ASM provides a scalable, objective framework for systematic architectural evaluation.
Methodology
The ASM approach quantifies four critical visual features—curvature, saturation, transparency, and symmetry based on their relative positions between opposing states. The methodology calculates comprehensive statistics including mean values, standard deviations, and outliers for each feature. This enables researchers to capture subtle design variations while maintaining interpretability. ASM operates without additional training or strict image constraints, highlighting its adaptability across diverse architectural datasets.
Data Collection and Preparation
A quantitative database of approximately 9,000 architectural images was constructed to evaluate ASM. Images were selected to cover a broad spectrum of styles, ensuring that the methodology could generalize across different design typologies. Each image was processed to extract relevant visual features, forming the basis for subsequent clustering and classification analyses. This large-scale dataset underpins the reliability and reproducibility of ASM metrics.
Analysis and Validation
ASM's effectiveness was validated through single-image and multi-image quantification, revealing consistent reflection of perceptual visual characteristics. Clustering analysis of the dataset demonstrated meaningful groupings, achieving an average silhouette score above 0.5. These results confirm that ASM can reliably capture stylistic similarities and differences in architectural designs, making it a robust tool for quantitative architectural research.
Applications in Design Evaluation
Beyond analysis, ASM facilitates practical applications in architectural research and practice. By providing interpretable and objective metrics, it enables classification, style comparison, and systematic evaluation of design portfolios. In this study, ASM-based classification achieved an accuracy of 87.2%, demonstrating its potential to support automated design categorization and informed decision-making in design evaluation.
Conclusion and Future Directions
ASM represents a significant advancement in data-driven architectural analysis, combining AI-powered feature extraction with quantitative evaluation methods. Its scalability, objectivity, and interpretability make it a promising tool for researchers and practitioners. Future work may explore extending ASM to other architectural elements, integrating multi-modal datasets, or incorporating real-time design feedback systems to further enhance data-driven architectural studies.
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