  After significant earthquakes, we can see images posted on social media
platforms by individuals and media agencies owing to the mass usage of
smartphones these days. These images can be utilized to provide information
about the shaking damage in the earthquake region both to the public and
research community, and potentially to guide rescue work. This paper presents
an automated way to extract the damaged building images after earthquakes from
social media platforms such as Twitter and thus identify the particular user
posts containing such images. Using transfer learning and ~6500 manually
labelled images, we trained a deep learning model to recognize images with
damaged buildings in the scene. The trained model achieved good performance
when tested on newly acquired images of earthquakes at different locations and
ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey.
Furthermore, to better understand how the model makes decisions, we also
implemented the Grad-CAM method to visualize the important locations on the
images that facilitate the decision.
