The recent advancement of generative foundational models has ushered in a new
era of image generation in the realm of natural images, revolutionizing art
design, entertainment, environment simulation, and beyond. Despite producing
high-quality samples, existing methods are constrained to generating images of
scenes at a limited scale. In this paper, we present MetaEarth, a generative
foundation model that breaks the barrier by scaling image generation to a
global level, exploring the creation of worldwide, multi-resolution, unbounded,
and virtually limitless remote sensing images. In MetaEarth, we propose a
resolution-guided self-cascading generative framework, which enables the
generating of images at any region with a wide range of geographical
resolutions. To achieve unbounded and arbitrary-sized image generation, we
design a novel noise sampling strategy for denoising diffusion models by
analyzing the generation conditions and initial noise. To train MetaEarth, we
construct a large dataset comprising multi-resolution optical remote sensing
images with geographical information. Experiments have demonstrated the
powerful capabilities of our method in generating global-scale images.
Additionally, the MetaEarth serves as a data engine that can provide
high-quality and rich training data for downstream tasks. Our model opens up
new possibilities for constructing generative world models by simulating Earth
visuals from an innovative overhead perspective.

MetaEarth 是一个生成性基础模型，通过分辨率引导的自级联生成框架，以及新颖的噪声采样策略，突破了图像生成的规模限制，实现了全球范围、多分辨率、无边界、几乎无限的遥感图像生成。