Landslide detection in real-time social media image streams

Ofli, Ferda; Imran, Muhammad; Qazi, Umair; Roch, Julien; Pennington, Catherine; Banks, Vanessa; Bossu, Remy. 2023 Landslide detection in real-time social media image streams. Neural Computing and Applications, 35. 17809-17819. https://doi.org/10.1007/s00521-023-08648-0

Before downloading, please read NORA policies.

Preview

Text (Open Access Paper) s00521-023-08648-0.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview

Abstract/Summary

Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s00521-023-08648-0
ISSN:	0941-0643
Date made live:	11 Jul 2023 11:55 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535364

Actions (login required)

View Item

Document Downloads

More statistics for this item...