TerraDiT-Ω：以任意地理空間基本元素進行衛星影像合成的統一空間控制

摘要

生成模型已取得顯著進展，但將其應用於衛星影像仍具挑戰性。不同於自然影像，衛星場景由空間複雜且語義明確的幾何結構所構成。先前研究透過採用密集柵格或稀疏提示來調整自然影像框架，在標註成本與精確度之間取得權衡，卻破壞了與地理資訊常用向量圖元的相容性。我們提出TerraDiT-Ω，這是一個統一的空間控制框架，可直接從任何原生地理空間圖元生成衛星影像。透過共同運用精確標註（多邊形、多段線）與粗略標註（邊界框、點），此模型支援在不同標註預算下實現可控佈局，不僅拓展了城市規劃等設計任務的適用性，同時保持與端到端地理空間AI工作流程的原生相容性。為在生成過程中有效運用這些圖元，我們提出幾何感知局部注意力——一種將明確幾何提示注入注意力空間的條件機制。在所有條件格式下，我們的方法 consistently 優於密集控制與稀疏控制基線。此外，此彈性使單一生成模型得以進行可控合成資料擴增，提升下游任務表現，包括土地覆蓋分割、物體偵測、道路圖提取及場景分類。程式碼、資料與權重已公開於 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.