Reconstructing building genealogy with visual intelligence.

The study of vernacular buildings involves recording, classifying, and analyzing large building populations to probe their environmental and cultural explanations. Traditionally, this process has relied on manual observation and researcher intuition, which can lead to selective and imprecise interpretations. Here we propose a computational framework that integrates visual intelligence to support the reconstruction of building genealogy. The framework is based on the premise that interpreting historical artefacts involves identifying a function from discrete data. We apply it to the stylistic classification of Singapore shophouses using frontage images of 1,276 historic shophouses in Singapore's Chinatown. Findings move beyond the chronological order established by the Urban Redevelopment Authority of Singapore in the 1990s and offer a critical perspective on the multi-ethnic character of Singapore shophouses. The study serves as a model for collaboration between studies in human settlements and computer science, demonstrating how leveraging the strengths of both fields can yield remarkable insights.