Landslide detection in real-time social media image streams

Ferda Ofli*, Muhammad Imran, Umair Qazi, Julien Roch, Catherine Pennington, Vanessa Banks, Remy Bossu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)17809-17819
Number of pages11
JournalNeural Computing and Applications
Volume35
Issue number24
DOIs
Publication statusPublished - Aug 2023

Keywords

  • Data-centric AI
  • Image classification
  • Landslide detection
  • Social media

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