A Generative Foundation Model for Global-Scale Remote Sensing Image Generation

Zhiping Yu, Chenyang Liu, Liqin Liu, Zhenwei Shi, Zhengxia Zou*

Beihang University

  [Preprint]       [Code]

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’s visuals from an innovative overhead perspective.

We constructed a world-scale remote sensing generative foundation model with over 600 million parameters based on the denoising diffusion paradigm. We propose a resolution-guided, self-cascading framework capable of generating scenes and resolutions for any global region. The generation process unfolds in multiple stages, starting with low-resolution images and advancing to high-resolution images. In each stage, the generation is conditioned on the low-resolution images and their associated spatial resolutions generated in the preceding stage.

Our MetaEarth can generate a variety of remote sensing scenes worldwide, including glaciers, snowfields, deserts, forests, beaches, farmlands, industrial areas, residential areas, etc.

To achieve the generation of large-scale remote sensing images of arbitrary sizes, we propose an unbounded image generation method including a memory-efficient sliding window generation pipeline and a noise sampling strategy. Our proposed unbounded method can greatly alleviate visual discontinuities caused by image block stitching, thereby achieving boundless and arbitrary-sized image generation.

Here are some examples of high-resolution large-scale images generated by our model (click to view the original sized images).

The self-cascading generation framework in our method enables the model to generate images with spatial resolution diversity.

Thanks to being trained on large-scale data, our MetaEarth possesses strong generalization capabilities and performs well even on unseen scenes. We create a "parallel world" and use a low-resolution map of "Pandora Planet" (generated by GPT4-V) as the initial condition for out model and then generated higher-resolution images sequentially. Despite our training data not covering such scenes, MetaEarth is still able to generate images with reasonable land cover distribution and realistic details.

    title={MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation},
    author={Zhiping Yu, Chenyang Liu, Liqin Liu, Zhenwei Shi, Zhengxia Zou},
    journal={arXiv preprint arXiv:2405.13570},