A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence

TL;DR

This paper introduces a real-time AI-powered social media monitoring system that automatically detects, geolocates, and categorizes landslide reports from Twitter to aid emergency response and research.

Contribution

The paper presents a novel online system that automatically filters, identifies, geolocates, and categorizes landslide reports from social media data in real time.

Findings

System has been operational since February 2020.

It effectively reduces irrelevant content and duplicates.

Provides real-time landslide information to partners.

Abstract

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.