A near-real-time global landslide incident reporting tool demonstrator using social media and artificial intelligence. (July 2022)

• Record Type: Journal Article
• Authors: Pennington, Catherine V.L.; Bossu, Rémy; Ofli, Ferda; Imran, Muhammad; Qazi, Umair; Roch, Julien; Banks, Vanessa J.
• Is Part Of: International journal of disaster risk reduction. Volume 77(2022)
• Publication Date: 2022-07
• Subjects: 6 max) -- Landslides -- Triggered-landslides -- Image-labelling -- Artificial intelligence -- Database; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2022.103089
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: 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%. The demonstrator model is currently running live https://landslide-aidr.qcri.org/service.php ; the next stage of development will incorporate stakeholder and user feedback.