Abstract
The availability of images of events almost in real time on social media has a prospect in many application developments. A humanitarian technology for disaster type and level assessment can be developed using the images and video available on social media. In this paper, we investigate the potential use of various available deep learning techniques to develop such an application. For our research, based on the use of publicly available image data, we have started collecting disaster images from various sources from South Asia. We created the South Asia Disaster (SAD) image dataset containing 493 images from various online news portals. Using the Keras as our framework to run our models: Visual Geometry Group (VGG-16 and VGG-19), Inception-V3, and Inception-ResNet-V2 (ResNet: Residual Network). However, to boost up the training speed, we dropped the fully connected layer and added a small, fully connected model. To identify the five different disasters: fire disaster, flood disaster, human disaster, infrastructure disaster, and natural disaster; our proposed method with VGG-16 model’s recognition accuracy was 83.37%, which is the highest accuracy on the SAD dataset.
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Sobhan, Z.: Dhaka Tribune, Kazi Anis Ahmed, viewed 20 June 2019 (2013). https://www.dhakatribune.com/
Anam, M.: The Daily Star, Mahfuz Anam, viewed 20 June 2019 (1991). https://www.thedailystar.net/
Rizk, Y., Awad, M., Castillo, C.: A computationally efficient multi-modal classification approach of disaster-related twitter images. In: 34th ACM/SIGAPP Symposium on Applied Computing, pp. 2050–2059. ACM, New York, USA (2019)
Avgerinakis, K., Moumtzidou, A., Andreadis, S., Michail, E., Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: Visual and textual analysis of social media and satellite images for flood detection. In: MediaEval 2017, pp. 3–5. Multimedia Satellite Task, Dublin, Ireland (2017)
Giannakeris, P., Avgerinakis, K., Karakostas, A., Vrochidis, S., Kompatsiaris, I.: People and vehicles in danger—a fire and flood detection system in social media. In: IEEE Image, Video, and Multidimensional Signal Processing. Zagorochoria Greece (2018)
Alam, F., Imran, M., Ofli, F.: Image4Act : online social media image processing for disaster response. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 601–604. ACM, New York, USA (2017)
Mouzannar, H., Awad, M.: Damage identification in social media posts using multimodal deep learning. In: 15th International Conference on Information Systems for Crisis Response and Management. Rochester, New York, USA (2018)
Nguyen, D.T., Ofli, F., Imran, M., Mitra, P.: Damage assessment from social media imagery data during disasters. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 569–576. ACM, New York, USA (2017)
Chollet, F.: Keras, GitHub, viewed on 15 Aug 2019 (2015). https://keras.io
Amin, M.A., Mohammed, M.K.: Overview of the ImageCLEF 2015 medical clustering task. In: CLEF (Working Notes) (2015)
Mouzannar, H., Rizk, Y., Awad, M.: Damage identification in social media posts using multimodal deep learning. In: 15th International Conference on Information Systems for Crisis Response and Management. Rochester, New York, USA (2018)
Muhammad, K., Ahmad, J., Baik, S.W.: Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288, 30–42 (2018). Elsevier Science Publishers B.V., Amsterdam, Netherlands
Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1943–1955. IEEE Computer Society, Washington, DC, USA (2016)
Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Computer Vision and Pattern Recognitioin, Las Vegas, NV, USA (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the Impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278–4284. AAAI Press, San Francisco, California, USA (2017)
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Arif, Omar, A., Ashraf, S., Rahman, A.K.M.M., Amin, M.A., Ali, A.A. (2020). A Comparative Study on Disaster Detection from Social Media Images Using Deep Learning. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_38
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DOI: https://doi.org/10.1007/978-981-15-2188-1_38
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