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

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

doi:10.1007/978-3-031-09917-5_4

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

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

^*Corresponding author for this work

Qatar Computing Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Web Engineering - 22nd International Conference, ICWE 2022, Proceedings
Editors	Tommaso Di Noia, In-Young Ko, Markus Schedl, Carmelo Ardito
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	49-65
Number of pages	17
ISBN (Print)	9783031099168
DOIs	https://doi.org/10.1007/978-3-031-09917-5_4
Publication status	Published - 2022
Event	22nd International Conference on Web Engineering, ICWE 2022 - Bari, Italy Duration: 5 Jul 2022 → 8 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13362 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Web Engineering, ICWE 2022
Country/Territory	Italy
City	Bari
Period	5/07/22 → 8/07/22

Keywords

Artificial intelligence
Computer vision
Image classification
Landslide detection
Online system
Real time
Social media

Access to Document

10.1007/978-3-031-09917-5_4

Cite this

Ofli, F., Qazi, U., Imran, M., Roch, J., Pennington, C., Banks, V., & Bossu, R. (2022). A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. In T. Di Noia, I.-Y. Ko, M. Schedl, & C. Ardito (Eds.), Web Engineering - 22nd International Conference, ICWE 2022, Proceedings (pp. 49-65). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13362 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09917-5_4

Ofli, Ferda ; Qazi, Umair ; Imran, Muhammad et al. / A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. editor / Tommaso Di Noia ; In-Young Ko ; Markus Schedl ; Carmelo Ardito. Springer Science and Business Media Deutschland GmbH, 2022. pp. 49-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{51eb391df882482395a4496c2d3dcc32,

title = "A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence",

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.",

keywords = "Artificial intelligence, Computer vision, Image classification, Landslide detection, Online system, Real time, Social media",

author = "Ferda Ofli and Umair Qazi and Muhammad Imran and Julien Roch and Catherine Pennington and Vanessa Banks and Remy Bossu",

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Ofli, F, Qazi, U, Imran, M, Roch, J, Pennington, C, Banks, V & Bossu, R 2022, A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. in T Di Noia, I-Y Ko, M Schedl & C Ardito (eds), Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13362 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 49-65, 22nd International Conference on Web Engineering, ICWE 2022, Bari, Italy, 5/07/22. https://doi.org/10.1007/978-3-031-09917-5_4

A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. / Ofli, Ferda; Qazi, Umair; Imran, Muhammad et al.
Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. ed. / Tommaso Di Noia; In-Young Ko; Markus Schedl; Carmelo Ardito. Springer Science and Business Media Deutschland GmbH, 2022. p. 49-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13362 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ofli, Ferda

AU - Qazi, Umair

AU - Imran, Muhammad

AU - Roch, Julien

AU - Pennington, Catherine

AU - Banks, Vanessa

AU - Bossu, Remy

PY - 2022

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N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Computer vision

KW - Image classification

KW - Landslide detection

KW - Online system

KW - Real time

KW - Social media

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DO - 10.1007/978-3-031-09917-5_4

M3 - Conference contribution

AN - SCOPUS:85135087189

SN - 9783031099168

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 49

EP - 65

BT - Web Engineering - 22nd International Conference, ICWE 2022, Proceedings

A2 - Di Noia, Tommaso

A2 - Ko, In-Young

A2 - Schedl, Markus

A2 - Ardito, Carmelo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Web Engineering, ICWE 2022

Y2 - 5 July 2022 through 8 July 2022

ER -

Ofli F, Qazi U, Imran M, Roch J, Pennington C, Banks V et al. A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence. In Di Noia T, Ko IY, Schedl M, Ardito C, editors, Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 49-65. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-09917-5_4