PRISM: Pseudo-Labeling and Region-Based Inpainting for Synthetic Change Detection Modeling

With the rapid advances in deep learning, there have been significant advances in the research of remote sensing, especially in the area of change detection. Change detection is crucial for monitoring urban development and environmental changes, but constructing high-quality, region-specific datasets remains a costly and labor-intensive challenge. Moreover, models trained on specific regions often underperform in new domains due to environmental differences. To address these limitations, we propose a novel framework, PRISM (Pseudo-labeling and Region-based Inpainting for Synthetic Change Detection Modeling), that generates synthetic post-images directly from single pre-images, eliminating the need for region-specific annotations or bitemporal image pairs. Our method segments building areas in the input image based on the foundation model, and the segmented regions are removed. The generative inpainting is applied to simulate realistic landscapes (e.g., grasslands, plains) that reflect hypothetical future changes. By leveraging morphological operations and descriptive text prompts, our approach ensures seamless integration of generated content with the surrounding context, producing realistic and region-adaptable datasets. Experimental results demonstrate that our method could reduce the dependency on annotated data, enhance adaptability across diverse regions, and enable efficient and scalable change detection modeling in an unsupervised setting.