Diffusion models have achieved state-of-the-art results on many modalities including images, speech, and video. However, existing models are not tailored to support remote sensing data, which is widely used in important applications including environmental monitoring and crop-yield prediction. Satellite images are significantly different from natural images -- they can be multi-spectral, irregularly sampled across time -- and existing diffusion models trained on images from the Web do not support them. Furthermore, remote sensing data is inherently spatio-temporal, requiring conditional generation tasks not supported by traditional methods based on captions or images. In this paper, we present DiffusionSat, to date the largest generative foundation model trained on a collection of publicly available large, high-resolution remote sensing datasets. As text-based captions are sparsely available for satellite images, we incorporate the associated metadata such as geolocation as conditioning information. Our method produces realistic samples and can be used to solve multiple generative tasks including temporal generation, superresolution given multi-spectral inputs and in-painting. Our method outperforms previous state-of-the-art methods for satellite image generation and is the first large-scale $\textit{generative}$ foundation model for satellite imagery.

翻译：扩散模型已在图像、语音和视频等多种模态上取得了最先进的结果。然而，现有模型并非专为支持遥感数据而设计，而遥感数据广泛应用于环境监测和作物产量预测等重要领域。卫星图像与自然图像存在显著差异——它们可能是多光谱的、随时间不规则采样的——而现有基于网络图像训练的扩散模型无法支持这些特征。此外，遥感数据本质上是时空性的，需要传统基于标题或图像的方法无法支持的条件生成任务。在本文中，我们提出了DiffusionSat，这是迄今为止最大的生成式基础模型，基于一系列公开可用的大规模高分辨率遥感数据集训练而成。由于卫星图像的基于文本的标题很少可用，我们将地理位置等相关元数据作为条件信息纳入。我们的方法能生成逼真的样本，并可用于解决多种生成任务，包括时序生成、基于多光谱输入的超分辨率以及图像修复。我们的方法在卫星图像生成方面优于以往的最先进方法，并且是首个针对卫星图像的大规模$\textit{生成式}$基础模型。