论文标题
一种用于使用人工智能在社交媒体上检测滑坡报告的实时系统
A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
论文作者
论文摘要
本文介绍了一个在线系统，该系统利用社交媒体数据实时利用与汇集相关的信息自动使用最先进的人工智能技术自动识别与滑坡相关的信息。设计的系统可以（i）通过消除重复和无关紧要的内容来减少信息过载，（ii）识别滑坡图像，（iii）推断图像的地理位置，以及（iv）对帐户共享信息的用户类型（组织或个人）分类。该系统于2020年2月在线部署在https://landslide-aidr.qcri.org/landslide_system.php上，以监视Live Twitter数据流，此后一直在不断运行，以便向诸如英国地质调查和欧洲地质核心境界的伴侣提供时间临界信息。我们相信该系统都可以为全球滑坡数据收集提供进一步的研究并支持全球滑坡地图，以促进应急和决策。
This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques. The designed system can (i) reduce the information overload by eliminating duplicate and irrelevant content, (ii) identify landslide images, (iii) infer geolocation of the images, and (iv) categorize the user type (organization or person) of the account sharing the information. The system was deployed in February 2020 online at https://landslide-aidr.qcri.org/landslide_system.php to monitor live Twitter data stream and has been running continuously since then to provide time-critical information to partners such as British Geological Survey and European Mediterranean Seismological Centre. We trust this system can both contribute to harvesting of global landslide data for further research and support global landslide maps to facilitate emergency response and decision making.
