Despite recent advancements in image generation, diffusion models still remain largely underexplored in Earth Observation. In this paper we show that state-of-the-art pretrained diffusion models can be conditioned on cartographic data to generate realistic satellite images. We provide two large datasets of paired OpenStreetMap images and satellite views over the region of Mainland Scotland and the Central Belt. We train a ControlNet model and qualitatively evaluate the results, demonstrating that both image quality and map fidelity are possible. Finally, we provide some insights on the opportunities and challenges of applying these models for remote sensing. Our model weights and code for creating the dataset are publicly available at https://github.com/miquel-espinosa/map-sat.

翻译：尽管图像生成领域近期取得了进展，扩散模型在地球观测中的应用仍很大程度上未被充分探索。本文表明，最先进的预训练扩散模型可通过地图数据进行条件化，从而生成逼真的卫星图像。我们提供了两个大型数据集，分别包含苏格兰本土及中部地区配对的OpenStreetMap图像与卫星视图。通过训练ControlNet模型并进行定性评估，我们证明了图像质量与地图保真度均可实现。最后，我们针对将这些模型应用于遥感领域的机遇与挑战提供了见解。模型权重及数据集创建代码已公开于https://github.com/miquel-espinosa/map-sat。