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10.1007_s00521-023-08648-0.pdf (1.7 MB)

Landslide detection in real-time social media image streams

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journal contribution
submitted on 2024-01-11, 09:12 and posted on 2024-01-15, 08:04 authored by Ferda Ofli, Muhammad Imran, Umair Qazi, Julien Roch, Catherine Pennington, Vanessa Banks, Remy Bossu

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.

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Published in: Neural Computing and Applications
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Open Access funding provided by the Qatar National Library.



  • English


Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU