Damage Assessment from Social Media Imagery Data During Disasters

Image | General | Response

This is a general-disaster dataset for damage severity assessment. It contains around 25,000 images of natural disasters from social media and Google Images and is introduced for multiclass (ordinal) classification.

  • ML task type: Multiclass (ordinal) classification
  • Data Source: Social Media (Twitter); Google Images
  • Size: ~ 25,000 Images
  • Timespan: Social Media: 2014 - 2016; Google Images: N/A
  • Geographical Coverage: Social Media: Philippines, Nepal, Ecuador, Areas affected by Hurricane Matthew; Google Images: Global
  • Baseline Information
  • Evaluated on: Bag-of-Visual-Words (BoVW), VGG16-fc7, and VGG16-fine-tuned
  • Metrics used: Accuracy, Precision, Recall, F1 score

Dat T Nguyen, Ferda Ofli, Muhammad Imran, and Prasenjit Mitra. Damage assessment from social media imagery data during disasters. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pages 569–576, 2017.