With increasing urbanization, in recent years there has been a growing interest and need in monitoring and analyzing urban flood events. Social media, as a new data source, can provide real-time information for flood monitoring. The social media posts with locations are often referred to as Volunteered Geographic Information (VGI), which can reveal the spatial pattern of such events. Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts. Apart from merely classifying posts as flood relevant or not, more detailed information, e.g. the flood severity, can also be extracted based on image interpretation. However, it has been less tackled and has not yet been applied for flood severity mapping.

In this paper, we propose a novel three-step process to extract and map flood severity information. First, flood relevant images are retrieved with the help of pre-trained convolutional neural networks as feature extractors. Second, the images containing people are further classified into four severity levels by observing the relationship between body parts and their partial inundation, i.e. images are classified according to the water level with respect to different body parts, namely ankle, knee, hip, and chest. Lastly, locations of the Tweets are used for generating a map of estimated flood extent and severity. This process was applied to an image dataset collected during Hurricane Harvey in 2017, as a proof of concept. The results show that VGI can be used as a supplement to remote sensing observations for flood extent mapping and is beneficial, especially for urban areas, where the infrastructure is often occluding water. Based on the extracted water level information, an integrated overview of flood severity can be provided for the early stages of emergency response.

随着城市化程度的提高，近年来，对监视和分析城市洪水事件的兴趣和需求日益增长。社交媒体作为一种新的数据源，可以为洪水监控提供实时信息。具有位置的社交媒体帖子通常称为“自愿地理信息”（VGI），可以揭示此类事件的空间格局。由于在社交媒体上共享的图像数量比以往任何时候都要多，因此最近的研究集中在通过分析除文本之外的图像来提取与洪水有关的帖子。除了仅将帖子分类为与洪水无关或无关之外，还可以基于图像解释来提取更详细的信息，例如洪水严重性。但是，它的解决方法较少，尚未应用于洪水严重程度制图。

在本文中，我们提出了一种新颖的三步过程来提取和映射洪水严重性信息。首先，借助预训练的卷积神经网络作为特征提取器来检索与洪水相关的图像。其次，通过观察人体部位及其局部浸水之间的关系，将包含人的图像进一步分为四个严重性级别，即，根据针对不同身体部位（即脚踝，膝盖，臀部和胸部）的水位对图像进行分类。最后，推文的位置用于生成估计洪水程度和严重程度的地图。此过程已应用于2017年哈维飓风期间收集的图像数据集，作为概念证明。结果表明，VGI可以用作洪水范围图的遥感观测的补充，并且对基础设施经常堵塞水的城市地区尤其有用。基于提取的水位信息，可以为紧急响应的早期阶段提供洪水严重程度的综合概述。