Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 45, 2019
https://doi.org/10.5194/ica-proc-2-45-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 45, 2019
https://doi.org/10.5194/ica-proc-2-45-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

Linking picture with text: tagging flood relevant tweets for rapid flood inundation mapping

Xiao Huang, Cuizhen Wang, and Zhenlong Li Xiao Huang et al.
  • University of South Carolina, Columbia, SC, USA

Keywords: convolutional neural network, flood mapping, social media

Abstract. Recent years have seen the growth of popularity in social media, especially in social media based disaster studies. During a flood event, volunteers may contribute useful information regarding the extent and the severity of a flood in a real-time manner, largely facilitating the process of rapid inundation mapping. However, considering that ontopic (flood related) social media only comprises a small amount in the entire social media space, a robust extraction method is in great need. Taking Twitter as targeted social media platform, this study presents a visual-textual approach to automatic tagging flood related tweets in order to achieve real-time flood mapping. Two convolutional neural networks are adopted to process pictures and text separately. Their outputs are further combined and fed to a visual-textual fused classifier. The result suggests that additional visual information from pictures leads to better classification accuracy and the extracted tweets, representing timely documentation of flood event, can greatly benefit a variety of flood mitigation approaches.

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