As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during hurricanes the area is largely covered by clouds and emergency managers must rely on nonintuitive flood visualizations for mission planning. To assist these emergency managers, we have created a deep learning pipeline that generates visual satellite images of current and future coastal flooding. 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. While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.
翻译:由于气候变化加剧了自然灾害的强度,社会需要更好的适应工具。例如,洪水是最频繁的自然灾害,但在飓风期间,该地区大部分为云层覆盖,应急管理人员必须依靠非直观的洪水视觉来进行任务规划。为了帮助这些应急管理人员,我们建立了一个深层次的学习管道,生成当前和未来沿海洪水的视觉卫星图像。我们开发了一个最先进的GAN,称为pix2pixHD, 从而产生与专家验证的风暴潮模型(NOAA SLOSH)产出相容的图像。通过评估与物理上的洪水地图相比的图像,我们发现我们拟议的框架在物理一致性和光现实主义两方面都优于基线模型。这项工作侧重于沿海洪水的视觉化,我们设想建立一个气候变化如何塑造地球的全球视觉化。