近期生成式AI模型在语言与视觉理解方面取得了显著突破。然而，尽管这些模型能够生成逼真的视觉内容，其空间尺度仍局限于有界环境，无法刻画地理环境在数千公里范围内的演化过程，亦难以建模大规模物理世界的空间结构。这一局限对地球观测与仿真中的超广域空间智能构成关键挑战，揭示了生成式AI领域一个更深层的缺口：当前进展主要依赖模型参数与训练数据规模的扩大，却忽视了空间尺度作为智能核心维度的重要性。受此缺失维度的启发，本文将空间尺度确立为大模型的新扩展轴，并提出MetaEarth3D——首个具备行星尺度空间一致性生成能力的生成式基础模型。以光学地球观测仿真作为验证平台，MetaEarth3D可生成多层级、无界且多样化的3D场景，覆盖大尺度地形、中尺度城市及细粒度街区。该模型基于全球分布的1000万张真实世界图像进行训练，在视觉真实感与地理空间统计真实感两方面均展现出强健性能。除生成能力外，MetaEarth3D还可作为生成式数据引擎，服务于超广域空间智能中的各类虚拟环境构建。我们认为，本研究有望推动下一代地球观测空间智能的发展。

Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate realistic visual content, their spatial scale remains confined to bounded environments, preventing them from capturing how geographic environments evolve across thousands of kilometers or from modeling the spatial structure of the large-scale physical world. This limitation poses a critical challenge for ultra-wide-area spatial intelligence in Earth observation and simulation, revealing a deeper gap in generative AI: progress has relied primarily on scaling model parameters and training data, while overlooking spatial scale as a core dimension of intelligence. Here, motivated by this missing dimension, we investigate spatial scale as a new scaling axis in foundation models and present MetaEarth3D, the first generative foundation model capable of spatially consistent generation at the planetary scale. Taking optical Earth observation simulation as a testbed, MetaEarth3D enables the generation of multi-level, unbounded, and diverse 3D scenes spanning large-scale terrains, medium-scale cities, and fine-grained street blocks. Built upon 10 million globally distributed real-world training images, MetaEarth3D demonstrates both strong visual realism and geospatial statistical realism. Beyond generation, MetaEarth3D serves as a generative data engine for diverse virtual environments in ultra-wide spatial intelligence. We argue that this study may help empower next-generation spatial intelligence for Earth observation.