论文标题
使用基于内容的过滤器在Twitter上查找相关的洪水图像
Finding Relevant Flood Images on Twitter using Content-based Filters
论文作者
论文摘要
及时对洪水等自然灾害的分析通常由于分布式传感器或传感器故障而受到有限的数据。同时，在Twitter等社交媒体平台上发布的大量事件图像中埋葬了大量信息。这些图像可用于记录和快速评估情况，并得出传感器（例如水污染程度）无法获得的代理数据。但是，并非所有在线发布的图像都适合或有用，以实现此目的。因此，我们提出了一种使用机器学习技术来查找与以下信息目标之一相关的Twitter图像的自动过滤方法：评估洪水泛滥的区域，淹没深度和水污染程度。过滤器不依赖于推文中存在的文本信息，而是直接分析图像内容。我们在两个重大洪水事件的案例研究中评估了两种不同的方法和各种特征的性能。与基于关键字的过滤器相比，我们的基于图像的过滤器能够大大提高结果的质量，从而平均平均平均精度从23％提高到53％。
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abundance of images of the event posted on social media platforms such as Twitter. These images could be used to document and rapidly assess the situation and derive proxy-data not available from sensors, e.g., the degree of water pollution. However, not all images posted online are suitable or informative enough for this purpose. Therefore, we propose an automatic filtering approach using machine learning techniques for finding Twitter images that are relevant for one of the following information objectives: assessing the flooded area, the inundation depth, and the degree of water pollution. Instead of relying on textual information present in the tweet, the filter analyzes the image contents directly. We evaluate the performance of two different approaches and various features on a case-study of two major flooding events. Our image-based filter is able to enhance the quality of the results substantially compared with a keyword-based filter, improving the mean average precision from 23% to 53% on average.
