| Summary: | Post-classification comparison using pre- and post-event remote-sensing images is a common way to quickly assess the impacts of a natural disaster on buildings. Both the effectiveness and efficiency of post-classification comparison heavily depend on the classifier’s precision and generalization abilities. In practice, practitioners used to train a novel image classifier for an unexpected disaster from scratch in order to evaluate building damage. Recently, it has become feasible to train a deep learning model to recognize buildings from very high-resolution images from all over the world. In this paper, we first evaluate the generalization ability of a global model trained on aerial images using post-disaster satellite images. Then, we systemically analyse three kinds of method to promote its generalization ability for post-disaster satellite images, i.e., fine-tune the model using very few training samples randomly selected from each disaster, transfer the style of postdisaster satellite images using the CycleGAN, and perform feature transformation using domain adversarial training. The xBD satellite images used in our experiment consist of 14 different events from six kinds of frequently occurring disaster types around the world, i.e., hurricanes, tornadoes, earthquakes, tsunamis, floods and wildfires. The experimental results show that the three methods can significantly promote the accuracy of the global model in terms of building mapping, and it is promising to conduct post-classification comparison using an existing global model coupled with an advanced transfer-learning method to quickly extract the damage information of buildings.
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