Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges

Abstract

The idea of ‘citizen as sensors’ has gradually become a reality over the past decade. Today, Volunteered Geographic Information (VGI) from citizens is highly involved in acquiring information on natural disasters. In particular, the rapid development of deep learning techniques in computer vision and natural language processing in recent years has allowed more information related to natural disasters to be extracted from social media, such as the severity of building damage and flood water levels. Meanwhile, many recent studies have integrated information extracted from social media with that from other sources, such as remote sensing and sensor networks, to provide comprehensive and detailed information on natural disasters. Therefore, it is of great significance to review the existing work, given the rapid development of this field. In this review, we summarized eight common tasks and their solutions in social media content analysis for natural disasters. We also grouped and analyzed studies that make further use of this extracted information, either standalone or in combination with other sources. Based on the review, we identified and discussed challenges and opportunities.

Keywords:

• Volunteered Geographic Information
• social media
• natural disaster
• spatiotemporal analysis

Acknowledgements

The authors would like to thank associate editor Dr. Urska Demsar and anonymous reviewers for their insightful comments and suggestions, which greatly improved this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support this study are available in GitHub with the link https://github.com/yuzzfeng/SocialMediaVGI4disasters.

Notes

1 Web of Science. https://www.webofknowledge.com [Accessed 07 May 2021].

2 Scopus. https://www.scopus.com/ [Accessed 07 May 2021].

3 Natural Disasters – Homeland Security. https://www.dhs.gov/natural-disasters [Accessed 07 May 2021].

4 SentiStrength. http://sentistrength.wlv.ac.uk/ [Accessed 07 May 2021].

5 OpenPose. https://github.com/CMU-Perceptual-Computing-Lab/openpose [Accessed 07 May 2021].

6 Humanitarian OpenStreetMap Team. https://www.hotosm.org/ [Accessed 07 May 2021].

7 MapAction – The humanitarian mapping charity. https://mapaction.org/ [Accessed 07 May 2021].

8 Ushahidi. https://www.ushahidi.com/ [Accessed 07 May 2021].

9 NAPSG Foundation Disaster Response Partnership – Crowdsourcing Projects. https://www.giscorps.org/napsg-crowdsource-photos/ [Accessed 07 May 2021].

10 Im2GPS Dataset: http://graphics.cs.cmu.edu/projects/im2gps/ [Accessed 07 May 2021].

11 Tiktok. https://www.tiktok.com/ [Accessed 07 May 2021].

Additional information

Funding

This work was supported by the German Federal Ministry of Education and Research (BMBF) under grant number [033W105A].

Notes on contributors

Yu Feng

Yu Feng is currently a postdoctoral researcher at the Institute of Cartography and Geoinformatics (ikg), Leibniz University Hannover. He received his M.Sc. and PhD in Geodesy and Geoinformatics from the same institution in 2015 and 2021. His research interests include social media VGI analysis, LiDAR data processing, map generalization, and deep learning.

Xiao Huang

Xiao Huang received his BS degree from Wuhan University in 2015. He obtained his Master’s degree from Georgia Institution of Technology in Geographic Information Science and Technology in 2016 and PhD in Geography from the University of South Carolina in 2020. He is currently an assistant professor in the Department of Geosciences at the University of Arkansas. His research interests cover GeoAI, deep learning, and human-environmental interactions.

Monika Sester

Monika Sester is a surveying engineer by training (University Karlsruhe) and earned her PhD on a topic of Machine Learning at the University of Stuttgart in 1995, and her habilitation in 2000 on the automatic generation of multiple representations of geodata. Since 2000 she has been professor and head of the Institute of Cartography and Geoinformatics (ikg) at Leibniz University Hannover. She and her group work on the automation of spatial data processing with methods from computational geometry, optimization and AI.