Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with $\approx$11 cm root mean square error.

洪水是最常见和灾难性的自然灾害之一，影响着全球数百万人。重要的是要创建准确的洪水地图，以计划（离线）和进行（实时）减灾和洪水救援行动。可以说，从社交媒体收集的图像可以为该任务提供有用的信息，否则这些信息将不可用。我们引入了一种计算机视觉系统，该系统可以根据洪水事件期间拍摄的社交媒体图像估算水深，以便实时（近）构建洪水地图。我们提出了一种多任务（深度）学习方法，其中使用回归和成对排名损失来训练模型。我们的方法是基于以下观察的：基于图像的洪水位估计的主要瓶颈是训练数据：这是困难的，并且需要付出大量的努力才能用正确的水深标注不受控制的图像。我们演示了如何从一小套带注释的水位和一大套较弱的注释中有效地学习预测变量，它们仅指示两个图像中的哪个水位较高，并且更容易获得。此外，我们提供了一个新的数据集，名为DeepFlood带有8145个带注释的地面图像，并显示了所提出的多任务方法可以根据单个的，众包的图像预测水位$\approx$均方根误差为11厘米。