Abstract
Natural disasters present a huge challenge for Government and non-Governmental organizations, as the individuals and communities are heavily affected. Disasters like earthquakes are seismic events that result in shaking of the earth’s surface causing large-scale death and destruction. Therefore, disaster management and control are of utmost importance. Nowadays individuals on site use social media platforms (like Twitter, Facebook, etc.) for sharing information and seeking remedy. If the extent of destruction can be estimated, one can assess the amount of relief needed. Thus, the data gathered from social media platforms can be used to evaluate the extremity of architectural damage induced by disasters like earthquakes. This paper proposes a CNN based segmentation of earthquake affected regions in images that are posted on online social media and classifying them into various classes according to the severity of damage. The approach is trained and tested using a dataset of images collected from social media posts during earthquakes and the results are encouraging.
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Layek, A.K., Chatterjee, A., Chatterjee, D., Biswas, S. (2020). Detection and Classification of Earthquake Images from Online Social Media. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_29
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DOI: https://doi.org/10.1007/978-981-13-9042-5_29
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