A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
This system addresses the need for efficient landslide monitoring and emergency response by reducing information overload and supporting global data harvesting, though it is incremental as it applies existing AI techniques to a specific domain.
The paper tackles the problem of real-time detection of landslide reports from social media by developing an online system that uses AI to filter, identify images, infer geolocation, and categorize user types, deployed since February 2020 to provide time-critical information to partners like the British Geological Survey.
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.