Details
| Original language | English |
|---|---|
| Pages (from-to) | 1275-1316 |
| Number of pages | 42 |
| Journal | International Journal of Geographical Information Science |
| Volume | 36 |
| Issue number | 7 |
| Early online date | 21 Mar 2022 |
| Publication status | Published - 2022 |
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
- natural disaster, social media, spatiotemporal analysis, Volunteered Geographic Information
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Library and Information Sciences
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: International Journal of Geographical Information Science, Vol. 36, No. 7, 2022, p. 1275-1316.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Extraction and analysis of natural disaster-related VGI from social media
T2 - review, opportunities and challenges
AU - Feng, Yu
AU - Huang, Xiao
AU - Sester, Monika
N1 - Funding Information: This work was supported by the German Federal Ministry of Education and Research (BMBF) under grant number [033W105A]. 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - natural disaster
KW - social media
KW - spatiotemporal analysis
KW - Volunteered Geographic Information
UR - http://www.scopus.com/inward/record.url?scp=85126735936&partnerID=8YFLogxK
U2 - 10.1080/13658816.2022.2048835
DO - 10.1080/13658816.2022.2048835
M3 - Review article
AN - SCOPUS:85126735936
VL - 36
SP - 1275
EP - 1316
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 7
ER -