通过从包含人的图像中解释水位，从嵌入式地理信息中绘制洪水严重程度图：飓风哈维的案例研究
Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: a case study of Hurricane Harvey
摘要：
随着城市化进程的加快，近年来对城市洪水事件的监测和分析越来越受到人们的关注和需求。社交媒体作为一种新的数据源，可以为洪水监测提供实时信息。带有位置的社交媒体帖子通常被称为嵌入式地理信息（VGI），它可以揭示此类事件的空间模式。由于社交媒体上分享的图像比以往任何时候都多，最近的研究集中在通过分析图像和文本来提取与洪水相关的帖子。除了仅将帖子分类为与洪水相关或不相关之外，还可以基于图像判读来提取更详细的信息，例如洪水严重性。然而，这一问题处理较少，尚未应用于洪水严重程度制图。在本文中，我们提出了一个新的三步过程中提取和映射洪水严重性信息。首先，洪水相关的图像检索的帮助下，预先训练的卷积神经网络作为特征提取器。其次，通过观察身体部位与其部分淹没的关系，将包含人物的图像进一步分为四个严重级别，即根据脚踝、膝盖、臀部和胸部等不同身体部位的水位对图像进行分类。最后，推文的位置用于生成估计的洪水范围和严重性的地图。该过程应用于2017年哈维飓风期间收集的图像数据集，作为概念验证。结果表明，VGI可以作为补充，遥感观测洪水范围映射，是有益的，特别是对城市地区，那里的基础设施往往是闭塞的水。基于提取的水位信息，可以为应急响应的早期阶段提供洪水严重程度的综合概述。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
