论文标题
检测野外自然灾害，损害和事件
Detecting natural disasters, damage, and incidents in the wild
论文作者
论文摘要
应对自然灾害，例如地震，洪水和野火，是由现场应急人员和分析师执行的一项费力的任务。社交媒体已成为低延迟数据源，以快速了解灾难情况。虽然大多数关于社交媒体的研究仅限于文本，但图像提供了更多信息，以了解灾难和事件场景。但是，不存在用于事件检测的大规模图像数据集。在这项工作中，我们介绍了事件数据集，其中包含由人类注释的446,684张图像，这些图像涵盖了各种场景的43起事件。我们采用一种基线分类模型来减轻假阳性错误，并对Flickr和Twitter的数百万个社交媒体图像进行图像过滤实验。通过这些实验，我们展示了如何使用事件数据集在野外发生事件来检测图像。代码，数据和模型可在http://incidentsdataset.csail.mit.edu上在线获得。
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.
