快速损失评估是应对组织在灾害发生时执行的核心任务之一，目的是了解道路、桥梁和建筑物等基础设施的损失程度。 这项工作分析了社会媒体图像内容的有用性，以执行快速损害评估在现实世界的灾难。 一个自动图像处理系统，与一个志愿者响应组织合作启动，处理了大约28万张图像，以了解灾难造成的破坏程度。 根据领域专家在灾难发生时分析 ~ 29k 系统处理图像的反馈信息，该系统的计算准确率达到76% 。 一个广泛的错误分析揭示了该系统面临的一些见解和挑战，这对于研究界推进这一研究方向至关重要。
原文题目：Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
原文：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.
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