Superimposing height-controllable and animated flood surfaces into street-level photographs for risk communication

Abstract

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.