论文标题
沿海洪水可视化的物理知识剂
Physics-informed GANs for Coastal Flood Visualization
论文作者
论文摘要
随着气候变化增加自然灾害的强度，社会需要更好的适应工具。例如，洪水是最常见的自然灾害，但是在飓风期间，该地区在很大程度上被云层所覆盖，紧急情况经理必须依靠非直觉的洪水可视化来进行任务计划。为了协助这些应急管理者，我们创建了一条深度学习管道，该管道生成了当前和未来沿海洪水的视觉卫星图像。我们提出了一个名为Pix2PixHD的最先进的gan，因此它产生的图像与专家验证的风暴潮模型（NOAA Slosh）的输出相吻合。通过评估相对于基于物理的洪水图的图像，我们发现我们所提出的框架在物理矛盾和光真相中都优于基线模型。尽管这项工作集中在沿海洪水的可视化上，但我们设想了全球可视化气候变化将如何影响我们的地球的创建。
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.
