Earthquakes: From Twitter Detection to EO Data Processing

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Andreadis, Stelios, Gialampoukidis, Ilias, Manconi, Andrea, Cordeiro, David, Conde, Vasco, Sagona, Manuela, Brito, Fabrice, Pantelidis, Nick, Mavropoulos, Thanassis, Grosso, Nuno, Vrochidis, Stefanos, Kompatsiaris, Ioannis
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blogs; Data analysis; Data processing; Detection; Digital media; Earth; Earth observation (EO); earthquake; Earthquake damage; Earthquakes; location identification; natural disaster management; Pipelines; Real-time systems; Satellite constellations; Satellites; Seismic activity; Social media; Social networking (online); Social networks; Submarine pipelines
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3149541

The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here, we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas most affected by earthquake damages.