Extreme flood events frequently threaten coastal and river communities, and communicating the potential impacts of such forecasts to their populations is crucial to protect property and human life. However, traditional methods to warn residents of forecasted flood events are often ignored or not fully understood. Recent works have produced 3D visualizations of flooding to better capture viewers’ attentions but tend to be expensive, visually unrealistic, or incapable of parameterizing water height. Here we propose an efficient and scientifically-grounded approach to generate realistic images and animations of a flood at any height composited with a photograph taken at street level. Using vehicular LIDAR point cloud and color photo data, we employ a convolutional neural network to generate a dense depth map across an image. We use 3D modeling software to automatically generate and render a water surface, along with its own depth map, at the appropriate height and orientation. The depth maps are used to composite the photo with the rendered water surface to generate the final images, and this process can be repeated to generate videos of rising coastal floodwaters with animated waves within minutes. These visualizations are striking, and the overall framework can be supported by any particular image collection or depth map construction methodology, making this an affordable and achievable approach to flood risk communication.

极端洪水事件经常威胁着沿海和河流社区，因此，将此类预报的潜在影响传达给他们的居民，对于保护财产和人类生命至关重要。但是，警告居民预报洪水事件的传统方法通常被忽略或未被完全理解。最近的工作产生了洪水的3D可视化效果，可以更好地捕捉观众的注意力，但往往价格昂贵，视觉上不现实或无法参数化水位。在这里，我们提出了一种有效且科学的方法，可以生成任意高度的洪水逼真的图像和动画，并结合在街道上拍摄的照片。使用车辆LIDAR点云和彩色照片数据，我们采用卷积神经网络在整个图像上生成密集的深度图。我们使用3D建模软件在适当的高度和方向上自动生成和渲染水面以及它自己的深度图。深度图用于将照片与渲染的水面合成，以生成最终图像，并且可以重复此过程以在几分钟内生成带有动画波的沿海洪水上升视频。这些可视化效果惊人，并且任何特定的图像收集或深度图构建方法都可以支持整个框架，从而使该方法成为洪水风险交流的可负担且可实现的方法。并且可以重复此过程，以在几分钟内生成带有动画波的沿海洪水上升视频。这些可视化效果惊人，并且任何特定的图像收集或深度图构建方法都可以支持整个框架，从而使该方法成为洪水风险交流的可负担且可实现的方法。并且可以重复此过程，以在几分钟内生成带有动画波的沿海洪水上升视频。这些可视化效果惊人，并且任何特定的图像收集或深度图构建方法都可以支持整个框架，从而使该方法成为洪水风险沟通的可负担且可实现的方法。