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A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence

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Web Engineering (ICWE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13362))

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Abstract

This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques. The designed system can (i) reduce the information overload by eliminating duplicate and irrelevant content, (ii) identify landslide images, (iii) infer geolocation of the images, and (iv) categorize the user type (organization or person) of the account sharing the information. The system was deployed in February 2020 online at https://landslide-aidr.qcri.org/landslide_system.php to monitor live Twitter data stream and has been running continuously since then to provide time-critical information to partners such as British Geological Survey and European Mediterranean Seismological Centre. We trust this system can both contribute to harvesting of global landslide data for further research and support global landslide maps to facilitate emergency response and decision making.

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Notes

  1. 1.

    https://gpm.nasa.gov/landslides/index.html (accessed on 12 February 2022).

  2. 2.

    https://developer.twitter.com/en/docs/twitter-api/v1/tweets/filter-realtime/guides/connecting (accessed on 24 March 2022).

  3. 3.

    https://redis.io/ (accessed on 24 March 2022).

  4. 4.

    https://spacy.io/usage/models (accessed on 24 March 2022).

  5. 5.

    The pre-trained model is available at http://places2.csail.mit.edu/models_places365/resnet50_places365.pth.tar (accessed on Jan 23, 2022).

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Acknowledgments

The British Geological Survey (UK Research and Innovation) granted supporting research funding through National Capability (Shallow Geohazards) and Innovation funding streams. European-Mediterranean Seismological Centre was partially funded by the European Union’s (EU) Horizon 2020 Research and Innovation Program under Grant Agreement RISE Number 821115. Opinions expressed in this article solely reflect the authors’ views; the EU is not responsible for any use that may be made of information it contains.

Author information

Authors and Affiliations

  1. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Ferda Ofli, Umair Qazi & Muhammad Imran

  2. European-Mediterranean Seismological Centre, Arpajon, France

    Julien Roch & Remy Bossu

  3. British Geological Survey, Keyworth, Nottinghamshire, UK

    Catherine Pennington & Vanessa Banks

  4. CEA, DAM, DIF, 91297, Arpajon, France

    Remy Bossu

Authors
  1. Ferda Ofli
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  2. Umair Qazi
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  3. Muhammad Imran
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  4. Julien Roch
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  5. Catherine Pennington
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  6. Vanessa Banks
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  7. Remy Bossu
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Corresponding author

Correspondence to Ferda Ofli .

Editor information

Editors and Affiliations

  1. Polytechnic University of Bari, Bari, Italy

    Tommaso Di Noia

  2. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    In-Young Ko

  3. Johannes Kepler University Linz, Linz, Austria

    Markus Schedl

  4. Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

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Ofli, F. et al. (2022). A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-09917-5_4

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