SPATIOTEMPORAL CONTRASTIVE REPRESENTATION LEARNING FOR BUILDING DAMAGE CLASSIFICATION

Automatic building damage assessment after natural disasters is important for emergency response. While existing supervised deep learning models achieved good performance on building damage classification, these models require massive human labels for training. Additionally, pre-trained models often fail to generalize well to new disaster events due to gaps between domains associated with training and testing data. In response, this study proposes a novel spatiotemporal contrastive representation learning model for learning features of building damages with big unlabeled data. Experimental results demonstrate superior performance of such features on classifying building damages resulting from various natural disasters (e.g., hurricanes, floods, wildfires, earthquakes, etc.) across different geographic locations worldwide, compared with the state-of-the-art supervised methods.