Unsupervised Domain Adaptation Using Generative Adversarial Network for Extreme Events Monitoring

In a traditional Machine Learning setting, it is assumed that the training and test dataset belong to the same feature space and the same distribution. For practical real-world scenarios, this is not always true. The model which is trained on the source dataset might give poor results on the target dataset (during testing/inferencing). We address this challenge using Unsupervised Domain Adaptation which deals with the situation in which the network is trained on the labeled source domain data and unlabeled data belonging to a different but related target domain and it gives improved results when tested on the target domain data. Also, generating data considering two different but related domains into account is a challenge. In this work, we address this challenge by proposing a framework for developing a cross-domain Generative Adversarial Network (GAN) with the aim of achieving unsupervised domain adaptation which applies to LiDAR data appertaining to extreme events. The dataset used for the study is the pre- and post-earthquake LiDAR (Light Detection and Ranging) dataset belonging to the Kumamoto earthquake of 2016. A comprehensive comparative analysis is conducted in this study to examine the efficacy of urban damage classification, both with and without the integration of unsupervised domain adaptation. We envisage that our framework can be used for performing urban damage classification in an efficient manner with increased accuracy.