Identifying infrastructure damage during earthquake using deep active learning.

Twitter provides important information for emergency responders in the rescue process during disasters. However, tweets containing relevant information are sparse and are usually hidden in a vast set of noisy contents. This leads to inherent challenges in generating suitable training data that are required for neural network models. In this paper, we study the problem of retrieving the infrastructure damage information from tweets generated from different location during crisis using the model actively trained on past but similar events. We combine RNN and GRU based model coupled with active learning that gets trained on most uncertain samples and captures the latent features of different data distribution. It reduces the uses of around 90% less training data, thereby significantly reducing the manual annotation efforts. We use the model pre-trained using active learning based approach to retrieve the infrastructure damage tweets originated from different regions. We obtain a minimum of 18% gain on F1-measure and considerably on other metrics over recent state-of-the-art IR techniques.