Detecting damaged buildings using real-time crowdsourced images and transfer learning.

ABSTRACT: 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 buildings 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 earthqua

SUBMITTER: Chachra G 

PROVIDER: S-EPMC9142599 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature