论文标题
事件1M：具有自然灾害，损害和事件的大型图像数据集
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
论文作者
论文摘要
随着地球经历全球变暖，洪水，龙卷风或野火等自然灾害越来越普遍。很难预测何时何地发生事件，因此及时的紧急响应对于挽救那些因破坏性事件而濒危的人的生命至关重要。幸运的是，技术可以在这些情况下发挥作用。社交媒体帖子可以用作低延迟数据源，以了解灾难的进展和后果，但是在没有自动化方法的情况下解析此数据是乏味的。先前的工作主要集中在基于文本的过滤上，但是基于图像和基于视频的过滤仍然在很大程度上尚未探索。在这项工作中，我们介绍了事件1M数据集，这是一个大规模的多标签数据集，其中包含977,088张图像，其中43个事件和49个位置类别。我们提供数据集构建，统计和潜在偏见的详细信息；介绍并训练一个模型以进行事件检测；并对Flickr和Twitter上的数百万张图像进行图像过滤实验。我们还提出了一些有关事件分析的应用程序，以鼓励并使未来的计算机愿景中的人道主义援助工作。代码，数据和模型可在http://incidentsdataset.csail.mit.edu上找到。
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Social media posts can be used as a low-latency data source to understand the progression and aftermath of a disaster, yet parsing this data is tedious without automated methods. Prior work has mostly focused on text-based filtering, yet image and video-based filtering remains largely unexplored. In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977,088 images, with 43 incident and 49 place categories. We provide details of the dataset construction, statistics and potential biases; introduce and train a model for incident detection; and perform image-filtering experiments on millions of images on Flickr and Twitter. We also present some applications on incident analysis to encourage and enable future work in computer vision for humanitarian aid. Code, data, and models are available at http://incidentsdataset.csail.mit.edu.
