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 diffcult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to effciently 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 ~11 cm root mean square error.

中文翻译：

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使用多任务排序方法从社交媒体图像预测水位

洪水是最频繁和灾难性的自然灾害之一，影响着全世界数百万人。创建准确的洪水地图以计划（离线）和执行（实时）洪水缓解和洪水救援行动非常重要。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，否则这些信息是不可用的。我们引入了一个计算机视觉系统，该系统可以从洪水事件期间拍摄的社交媒体图像中估计水深，以便（近）实时构建洪水地图。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序损失训练模型。我们的方法的动机是观察到基于图像的洪水位估计的主要瓶颈是训练数据：用正确的水深注释不受控制的图像很困难，需要付出很多努力。我们演示了如何从一小组带注释的水位和一组较大的较弱的注释中有效地学习预测器，这些注释仅指示两个图像中的哪个水位更高，并且更容易获得。此外，我们提供了一个名为 DeepFlood 的新数据集，其中包含 8145 个带注释的地面图像，并表明所提出的多任务方法可以从单个众包图像中预测水位，均方根误差约为 11 厘米。