TerraDiT-Ω: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive

Generative models have achieved remarkable progress, yetapplying them to satellite imagery remains challenging. Unlike natu-ral imagery, satellite scenes are structured by spatially complex andsemantically distinct geometries. Prior work addresses this complexityby adapting natural image frameworks using dense rasters or sparseprompts, trading off annotation cost and fidelity while breaking com-patibility with vector primitives commonly used to represent geographicinformation. We introduce TerraDiT-Ω, a unified spatial control frame-work that generates satellite imagery directly from any native geospatialprimitive. By jointly leveraging precise annotations (polygons, polylines)and coarser ones (bounding boxes, points), the model supports control-lable layouts across varying annotation budgets, broadening applicabilityto design tasks such as urban planning while remaining naturally com-patible with end-to-end GeoAI workflows. To effectively leverage theseprimitives during generation, we propose Geometry-Aware Local Atten-tion, a conditioning mechanism that injects explicit geometric cues intothe attention space. Across all conditioning formats, our approach con-sistently outperforms both dense-control and sparse-control baselines.Furthermore, this flexibility enables controllable synthetic data augmen-tation using a single generative model, improving downstream perfor-mance on land-cover segmentation, object detection, road graph extrac-tion, and scene classification. Code, data, and weights are available at