Automated Building Classification for Scalable Urban Material Stock Assesment

Abstract: Soviet-era apartment blocks in Eastern Europe are being demolished, generating mixed waste and limiting circular reuse. Existing assessments rely on generic archetypes and lack element-level material detail. This study develops an automated algorithm to detect standardised Soviet building series for scalable material stock analysis. A distance-based classification using apartment layouts, floor area, height, and staircases was validated on 337 buildings and applied to Estonia’s building stock. The model achieved 70% series-level accuracy, with dominant series represent 61% of the stock. This element-level quantification enables targeted deconstruction and higher material recovery for circular economy planning.