The development of a system that monitors social media continuously for general landslide-related content using a landslide classification model to identify and retain the most relevant information is described and validated. The system harvests photographs in real-time from these data and tags each image as landslide or not-landslide. A training model was developed with input from computer scientists, geologists (landslide specialists) and social media specialists to establish a large image dataset that has then been applied to the live Twitter data stream. The preliminary model was developed by training a convolutional neural network on the dataset. Quantitative verification of the system’s performance during a real-world deployment shows that the system can detect landslide reports with Precision = 76%.
System: https://landslide-aidr.qcri.org/service.php
Paper 1: Landslide detection in real-time social media image streams
Paper 2: A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence