A Scale-Adaptive Framework for Building Generalization via Instruction-Guided Diffusion and Geometry-Aware Vectorization

Abstract Map generalization aims to adapt detailed geospatial data to visualization at different map scales while preserving legibility and geometric consistency. Recent learning-based approaches have shown promising results, but they typically require training separate models for each target scale and produce raster outputs that lack geometric regularity. This paper proposes an instruction-guided diffusion framework for raster-based building map generalization that enables scale-adaptive generalization within a single model. By conditioning a latent diffusion model with scale-indicative textual instructions, the framework allows prompt-driven control of generalization behavior across multiple and previously unseen target scales, while implicitly capturing the effects of multiple generalization operators, such as simplification, aggregation, and selection. The raster-based generalization results are subsequently converted to vector maps using frame-field-based vectorization, producing building footprints with preserved orthogonality and parallelism. Experiments on multi-scale building map generalization demonstrate that the proposed method yields vectorized results with substantially improved geometric regularity over existing baselines. Quantitative evaluations at both pixel and polygon levels confirm enhanced boundary regularity, rectangularity, and parallelism. Cross-scale experiments further indicate smooth scale-dependent behavior across the evaluated seen and unseen intermediate scales.