  Climate change has led to an increased frequency of natural disasters such as
floods and cyclones. This emphasizes the importance of effective disaster
monitoring. In response, the remote sensing community has explored change
detection methods. These methods are primarily categorized into supervised
techniques, which yield precise results but come with high labeling costs, and
unsupervised techniques, which eliminate the need for labeling but involve
intricate hyperparameter tuning. To address these challenges, we propose a
novel unsupervised change detection method named Prototype-oriented
Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures
changes by comparing features from pre-event, post-event, and
prototype-oriented change synthesis images via a foundational model, and
refines results using the Segment Anything Model (SAM). Although PUCD is an
unsupervised change detection, it does not require complex hyperparameter
tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the
disaster dataset and it achieves state-of-the-art performance compared to other
methods on the LEVIR-Extension dataset.
