A vision-intelligent framework for mapping the genealogy of vernacular architecture

2025-05-24

AI Summary

Traditional vernacular architecture studies rely heavily on subjective expertise, hindering objective elucidation of their phylogenetic structures and socio-cultural logics. This study introduces “quantitative vernacular phylogenetics,” a novel methodological framework applied to 1,277 historic shophouses in Singapore’s Chinatown. Integrating computer vision, morphometric analysis, clustering algorithms, and graph neural networks, it constructs a spatiotemporally coupled morphological evolution network—departing from conventional linear periodization and unidirectional assimilation assumptions. The approach identifies nine distinct morphological clusters, empirically revealing dynamic, parallel evolutionary trajectories shaped by multi-ethnic interactions. Results significantly enhance the objectivity, systematicity, and explanatory power of architectural phylogenetic analysis. By transcending disciplinary boundaries, the framework offers a transferable methodology for interdisciplinary heritage research. (136 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evolutionary Learning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The study of vernacular architecture involves recording, ordering, and analysing buildings to probe their physical, social, and cultural explanations. Traditionally, this process is conducted manually and intuitively by researchers. Because human perception is selective and often partial, the resulting interpretations of architecture are invariably broad and loose, often lingering on form descriptions that adhere to a preset linear historical progression or crude regional demarcations. This study proposes a research framework by which intelligent technologies can be systematically assembled to augment researchers' intuition in mapping or uncovering the genealogy of vernacular architecture and its connotative socio-cultural system. We employ this framework to examine the stylistic classification of 1,277 historical shophouses in Singapore's Chinatown. Findings extend beyond the chronological classification established by the Urban Redevelopment Authority of Singapore in the 1980s and 1990s, presenting instead a phylogenetic network to capture the formal evolution of shophouses across time and space. The network organises the shophouse types into nine distinct clusters, revealing concurrent evidences of cultural evolution and diffusion. Moreover, it provides a critical perspective on the multi-ethnic character of Singapore shophouses by suggesting that the distinct cultural influences of different ethnic groups led to a pattern of parallel evolution rather than direct convergence. Our work advances a quantitative genealogy of vernacular architecture, which not only assists in formal description but also reveals the underlying forces of development and change. It also exemplified the potential of collaboration between studies in vernacular architecture and computer science, demonstrating how leveraging the strengths of both fields can yield remarkable insights.

Problem

• Automating vernacular architecture genealogy mapping with AI
• Overcoming subjective manual analysis in architectural studies
• Quantifying cultural evolution in Singapore shophouse styles

Innovation

• Vision-intelligent framework for architectural genealogy
• Phylogenetic network for formal evolution analysis
• Quantitative genealogy revealing cultural evolution forces