Ready to Sim? Inverse Parametric Building Modeling via Universal Anchor Representation for Urban Simulation
Abstract
Physics-based urban simulations require editable, topology-valid building representations, yet urban data are typically available only as images or segmentation masks. We propose Universal Anchor Representation (UAR), a normalized hierarchical parameterization that constrains inference to bounded coordinates (t,s) \in [0,1]2 while guaranteeing structural connectivity by construction. We pair UAR with a domain-specific language and compiler to produce executable parametric procedural graphs, and develop a vision-language model (VLM) guided pipeline with overlay-based refinement to recover them from top-view building images. Experiments on 110 real urban buildings spanning 11 morphological categories demonstrate that UAR-based generation achieves 75.2% F1 score and 100% compilation success, substantially outperforming absolute-coordinate generation, which attains 43.1% F1 and 57% success under identical model and prompt configurations. Wind simulation case studies further confirm that recovered parameters yield physically interpretable changes in flow fields under parametric edits.
Authors: Jaeyeon Kim, Po-Yen Lai, Jian Cheng Wong, Chin Chun Ooi, Yew-Soon Ong, Ivor Wai-Hung Tsang
Published in: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2026)