[26] Semi-Automated Dataset Generation for Residential Buildings Using Graph-Based Topological Modelling

Paper Description extracted from PDF · abstract · 147 words

Most of Italy’s residential building stock predates contemporary structural safety and energy efficiency regulatory frameworks. Today, policymakers face the challenge of choosing whether to prioritise renovation or opt for demolition and reconstruction; both options carry significant socio-economic and environmental consequences and require extensive knowledge of the built heritage. However, detailed architecture-specific data remain scarce, as existing databases lack granular information. Moreover, traditional urbanlevel knowledge mapping approaches may be resource-intensive. To address this data gap, this study proposes a semi-automated methodology for generating graph-based digital models representing residential building floor plans. Using graph theory, floor spatial layouts are mapped into connectivity graphs and transformed into topological models. These models are enriched with functional data about spaces by assigning conditional topological rules based on node centrality metrics. The method was tested on 98 buildings in Bologna, Italy, yielding an 89.8% success rate and demonstrating its effectiveness in datalimited contexts.