TerraDiT-Ω：使用任意地理空间基元的卫星图像合成统一空间控制

摘要

生成模型已取得显著进展，但将其应用于卫星图像仍具挑战性。与自然图像不同，卫星场景由空间复杂且语义分明的几何结构组成。现有工作通过采用密集栅格或稀疏提示来适配自然图像框架，以平衡标注成本与保真度，但破坏了与地理信息常用矢量基元的兼容性。我们提出TerraDiT-Ω——一种统一的空间控制框架，可直接从任意原生地理基元生成卫星图像。通过联合利用精确标注（多边形、折线）与粗粒度标注（边界框、点），该模型支持在不同标注预算下实现可控布局，扩展至城市规划等设计任务，同时保持与端到端地理空间AI工作流的天然兼容性。为在生成过程中有效利用这些基元，我们提出几何感知局部注意力——一种将显式几何线索注入注意力空间的调节机制。在所有条件格式下，我们的方法均显著优于密集控制和稀疏控制基线。此外，这种灵活性使得单一生成模型能够实现可控的合成数据增强，从而提升下游任务性能，包括土地覆盖分割、目标检测、道路图提取和场景分类。代码、数据和模型权重已开源至 https://github.com/mvrl/TerraDiT。

English

Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging. Unlike natural imagery, satellite scenes are structured by spatially complex and semantically distinct geometries. Prior work addresses this complexity by adapting natural image frameworks using dense rasters or sparse prompts, trading off annotation cost and fidelity while breaking compatibility with vector primitives commonly used to represent geographic information. We introduce TerraDiT-Ω, a unified spatial control framework that generates satellite imagery directly from any native geospatial primitive. By jointly leveraging precise annotations (polygons, polylines) and coarser ones (bounding boxes, points), the model supports controllable layouts across varying annotation budgets, broadening applicability to design tasks such as urban planning while remaining naturally compatible with end-to-end GeoAI workflows. To effectively leverage these primitives during generation, we propose Geometry-Aware Local Attention, a conditioning mechanism that injects explicit geometric cues into the attention space. Across all conditioning formats, our approach consistently outperforms both dense-control and sparse-control baselines. Furthermore, this flexibility enables controllable synthetic data augmentation using a single generative model, improving downstream performance on land-cover segmentation, object detection, road graph extraction, and scene classification. Code, data, and weights are available at https://github.com/mvrl/TerraDiT.