Large-scale building renovation programmes require extensive knowledge of the existing building stock. However, collecting detailed information building-by-building is time-consuming, costly, and often difficult at district or city scale. The solution addresses this challenge by enabling automated and scalable identification of building typologies using existing visual and spatial datasets. By clustering buildings with similar façade and roof characteristics, the approach supports the development of industrialised retrofit strategies and standardised renovation solutions.