As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, and better tools for flood risk communication could increase the support for flood-resilient infrastructure development. Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery. We propose the first deep learning pipeline to ensure physical-consistency in synthetic visual satellite imagery. We advanced a state-of-the-art GAN called pix2pixHD, such that it produces imagery that is physically-consistent with the output of an expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. We envision our work to be the first step towards a global visualization of how the climate challenge will shape our landscape. Continuing on this path, we show that the proposed pipeline generalizes to visualize reforestation. We also publish a dataset of over 25k labelled image-triplets to study image-to-image translation in Earth observation.

翻译：随着气候变化加剧自然灾害的强度，社会亟需更好的适应工具。以洪水为例，作为最频发的自然灾害，改进洪水风险沟通工具可提升对防洪基础设施建设的支持力度。本研究旨在通过将沿海洪水模型的输出可视化为卫星图像，增强大规模气候影响的视觉化沟通。我们提出了首个确保合成卫星图像物理一致性的深度学习管道。我们对当前最先进的生成对抗网络pix2pixHD进行改进，使其生成的图像与经专家验证的暴潮模型（NOAA SLOSH）输出保持物理一致性。通过将图像与基于物理的洪水地图进行比对评估，发现我们提出的框架在物理一致性和逼真度方面均优于基线模型。我们期望这项工作成为全球可视化气候变化如何重塑地貌的初步探索。沿着这一方向，我们展示了所提出管道在可视化森林再造任务中的泛化能力。此外，我们发布了包含超过2.5万组标注图像三元组的数据集，用于研究地球观测领域的图像到图像转换。