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Area of Science:

  • Computer Science
  • Earthquake Engineering
  • Social Media Analysis

Background:

  • Social media platforms are increasingly used to share real-time information during natural disasters.
  • Images shared online after earthquakes offer valuable data on structural damage.
  • Manual analysis of this data is time-consuming and inefficient for rapid response.

Purpose of the Study:

  • To develop an automated system for extracting images of damaged buildings from social media after earthquakes.
  • To identify specific user posts containing relevant visual information.
  • To facilitate faster damage assessment and inform rescue operations.

Main Methods:

  • Utilized transfer learning with a deep learning model trained on approximately 6,500 manually labeled images.
  • Developed a model to recognize scenes containing damaged buildings.
  • Implemented Grad-CAM for visualizing image regions critical to the model's decision-making process.

Main Results:

  • The trained deep learning model demonstrated good performance in identifying damaged buildings in newly acquired earthquake images.
  • The system was successfully tested in near real-time on Twitter data following the 2020 M7.0 Turkey earthquake.
  • Grad-CAM visualizations provided insights into the model's decision-making logic.

Conclusions:

  • Automated extraction of damaged building images from social media is feasible and effective.
  • This approach can significantly enhance the speed and scope of post-earthquake damage assessment.
  • The developed model shows potential for real-time disaster response support.