发表机构
Washington University in St. Louis（圣路易斯华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有卫星图像生成模型无法生成尺度与空间一致的完整金字塔的问题，提出多尺度瓦片补全任务，开发Genesis生成引擎，结合垂直超分辨率与水平外绘算子，引入dense500数据集，实现多尺度一致的卫星图像合成。
AI 中文摘要
地球观测本质上是多尺度的；地理空间任务涵盖不同分辨率，卫星图像被组织成级联的瓦片金字塔，将精细细节嵌套在广阔的覆盖范围内。然而，现有的卫星图像生成模型仅沿单一轴运行：要么缩放以提升单个瓦片的分辨率，要么平移以在固定尺度上扩展图像。因此，现有方法无法生成在尺度和空间上都保持一致的完整金字塔，其中高缩放级别的瓦片必须与其所细化的粗粒度上下文以及相邻瓦片保持一致。针对这一空白，我们提出了一项新任务：多尺度瓦片补全：给定一组在任意缩放级别和位置的稀疏种子瓦片，合成一个完整、均匀的四叉树，使其在尺度和空间上都保持全局一致。我们通过Genesis来处理该任务，这是一种生成引擎，通过在四叉树上组合两个专用算子将两个轴结合起来：一个垂直超分辨率模型和一个基于水平掩码的外绘模型，生成的金字塔在缩放级别间保持一致，且相邻瓦片间无缝衔接。每个算子在其对应的子任务上都达到了最先进的结果，该引擎可将稀疏种子从任何初始配置传播为无缝的多分辨率地图。为了评估该任务并对Genesis进行基准测试，我们引入了dense500，这是一个涵盖不同地理区域的完全观测的多尺度金字塔数据集，同时还提出了一套金字塔级别的指标。代码、模型和我们的数据集可在this https URL获取。
英文摘要
Earth observation is fundamentally multi-scale; geospatial tasks span varied resolutions, and satellite imagery is organized into cascading tile pyramids that nest fine detail within wide coverage. Current generative models of satellite imagery, however, operate along a single axis: they either zoom to enhance a single tile's resolution or pan to extend imagery at a fixed scale. As a result, no existing method produces a complete pyramid that stays consistent across both scale and space, where a high-zoom tile must agree with the coarse context it refines and with the neighbors it meets. Motivated by this gap, we introduce a new task, multi-scale tile completion: given a sparse set of seed tiles at arbitrary zoom levels and positions, synthesize a complete, uniform quadtree that is globally consistent across both scale and space. We approach this task with Genesis, a generative engine that brings both axes together by composing two specialized operators over the quadtree: a vertical super-resolution model and a horizontal mask-based outpainting model, producing pyramids that are consistent across zoom levels and seamless across neighboring tiles. Each operator achieves state-of-the-art results on its subtask, and the engine propagates sparse seeds into seamless, multi-resolution maps from any initial configuration. To evaluate the task and benchmark Genesis, we introduce dense500, a fully observed multi-scale pyramid dataset spanning diverse geographic regions, together with a suite of pyramid-level metrics. Code, models, and our dataset are available at https://github.com/mvrl/genesis.
CommentsAccepted to SIGSPATIAL 2026: Application Track (Oral)
