论文标题
使用社交媒体图像进行快速损害评估，通过结合人力和机器智能
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
论文作者
论文摘要
快速损害评估是响应组织在灾难开始时执行的核心任务之一，以了解对道路，桥梁和建筑物等基础设施的损害规模。这项工作分析了社交媒体图像内容的有用性，以在现实世界中进行快速损害评估。与志愿者响应组织合作激活的自动图像处理系统，处理了〜280K图像，以了解灾难造成的损害程度。该系统的准确度是根据从域名专家那里收到的反馈计算的76％的准确性，他们在灾难期间分析了〜29k系统处理的图像。广泛的错误分析揭示了系统面临的几种见解和挑战，这对于研究界推进这一研究的研究至关重要。
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
