SynUrbanSAT: Controllable Satellite Data Synthesis for Urban Semantic 3D Reconstruction

Urban semantic 3D reconstruction from monocular satellite imagery requires geographically diverse data with aligned semantic and height supervision, yet acquiring precise annotations at scale remains costly. Existing synthesis methods struggle to jointly provide real-world urban layouts, realistic appearance, and spatially aligned supervision. To address this gap, we propose SynUrbanSAT, a scalable and controllable pipeline that combines OpenStreetMap (OSM)-based 3D scene construction with diffusion-based satellite image synthesis. A heterogeneous dual-condition adapter (HDCA) integrates categorical semantics and continuous metric heights to guide the generation of realistic satellite imagery. The pipeline supports controllable data augmentation by varying building-height distributions, vegetation configurations, viewpoints, and appearance. It enables scalable synthesis of geographically diverse data across cities with a single trained generator, while offering the option to train a dedicated generator on limited target-city imagery for local data augmentation. To support a broad range of downstream urban perception and reconstruction tasks, we construct SynUrbanSAT-20, comprising 20,097 samples across 20 cities with synthetic RGB images and aligned semantic labels, height maps, polygon maps, point clouds, and 3D meshes. Experiments demonstrate that our image generation method improves visual fidelity and the utility of synthetic data for downstream learning. The complete pipeline further outperforms the evaluated synthetic training sources in urban semantic 3D reconstruction.