A near-real-time global landslide incident reporting tool demonstrator using social media and artificial intelligence

Pennington, Catherine V.L.; Bossu, Rémy; Ofli, Ferda; Imran, Muhammad; Qazi, Umair; Roch, Julien; Banks, Vanessa J.. 2022 A near-real-time global landslide incident reporting tool demonstrator using social media and artificial intelligence. International Journal of Disaster Risk Reduction, 77, 103089. https://doi.org/10.1016/j.ijdrr.2022.103089

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ijdrr.2022.103089
ISSN:	22124209
Date made live:	10 Jun 2022 11:06 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532729

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