TerraDiT-Ω: 基于任意地理空间基元的卫星图像合成统一空间控制框架
阅读原文· arxiv.orgTerraDiT-Ω是一个统一空间控制框架,可直接利用任意地理空间基元(多边形、折线、边界框、点)进行卫星图像生成。它联合使用精确和粗略标注,适应不同标注预算,适用于城市规划等设计任务。提出Geometry-Aware Local Attention机制,将几何线索注入注意力空间。在所有格式上优于密集和稀疏控制基线。该框架支持可控数据增强,提升土地覆盖分割、目标检测、道路图提取、场景分类等下游性能。代码、数据、权重已开源。
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.