Natural disasters detection in social media and satellite imagery: a survey

• Published in: Multimedia tools and applications Vol. 78; no. 22; pp. 31267 - 31302
• Main Authors: Said, Naina, Ahmad, Kashif, Riegler, Michael, Pogorelov, Konstantin, Hassan, Laiq, Ahmad, Nasir, Conci, Nicola
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2019; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Digital media; Disasters; Domains; Image detection; Information retrieval; Literature reviews; Multimedia; Multimedia Information Systems; Natural disasters; Satellite imagery; Satellites; Social networks; Special Purpose and Application-Based Systems; Deep learning; Information retrieval; Satellite; CNN; Natural disasters; Social media
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-07942-1

The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disaster-related multimedia content. Satellite imagery has also been widely explored for disasters analysis. In this paper, we survey the existing literature on disaster detection and analysis of the retrieved information from social media and satellites. Literature on disaster detection and analysis of related multimedia content on the basis of the nature of the content can be categorized into three groups, namely (i) disaster detection in text; (ii) analysis of disaster-related visual content from social media; and (iii) disaster detection in satellite imagery. We extensively review different approaches proposed in these three domains. Furthermore, we also review benchmarking datasets available for the evaluation of disaster detection frameworks. Moreover, we provide a detailed discussion on the insights obtained from the literature review, and identify future trends and challenges, which will provide an important starting point for the researchers in the field.