Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges. Issue 7 (3rd July 2022)

• Record Type: Journal Article
• Authors: Feng, Yu; Huang, Xiao; Sester, Monika
• Is Part Of: International journal of geographical information science. Volume 36:Issue 7(2022)
• Page Start: 1275
• Page End: 1316
• Publication Date: 2022-07-03
• Subjects: Volunteered Geographic Information -- social media -- natural disaster -- spatiotemporal analysis; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2048835
• Languages: English
• ISSNs: 1365-8816

Abstract

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.