Unconstrained Flood Event Detection Using Adversarial Data Augmentation

Samira Pouyanfar, Yudong Tao, Saad Sadiq, Haiman Tian, Yuexuan Tu, Tianyi Wang, Shu Ching Chen, Mei Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Nowadays, the world faces extreme climate changes, resulting in an increase of natural disaster events and their severities. In these conditions, the necessity of disaster information management systems has become more imperative. Specifically, in this paper, the problem of flood event detection from images with real-world conditions is addressed. That is, the images may be taken in several conditions, including day, night, blurry, clear, foggy, rainy, different lighting conditions, etc. All these abnormal scenarios significantly reduce the performance of the learning algorithms. In addition, many existing image classification methods use datasets that usually include high-resolution images without considering real-world noise. In this paper, we propose a new image classification framework based on adversarial data augmentation and deep learning algorithms to address the aforementioned problems. We validate the performance of the flood event detection framework on a real-world noisy visual dataset collected from social networks.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538662496
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China


  • Flood event detection
  • deep learning
  • generative adversarial networks
  • style transfer

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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