SnapQuake: Damage Detection in Snapchat Videos for Earthquake Assessment

Designing practical algorithms for damage detection in satellite images requires a substantial and well-labeled dataset for training, validation, and testing. In this paper, we collect GAZADeepDav: a high-resolution PlanetScope satellite imagery dataset with 7264 tiles for no damage and 6196 tiles for damage . This work is delving into the steps of collecting the dataset, Geotagging and employing deep learning architectures to distinguish damage in war zones while also providing valuable insights for researchers undertaking similar tasks in real-world applications. The experimental results on this dataset, using SqueezeNet architecture, yielded an impressive 98.95\%  accuracy in classifying damage. The study also explored augmenting SqueezeNet with Bidirectional Long Short-Term Memory (BiLSTM) layers, resulting in a heightened accuracy of 99.10\%. The combination of SqueezeNet and BiLSTM exemplifies the potential for advanced model architectures to enhance damage classification accuracy further.