Updating urban-area maps is crucial for urban planning and development. Traditional methods of updating urban-area maps based on aerial photography are labor-intensive and struggle to keep pace with rapid urban development. Automated algorithms for detecting new and removed buildings based on bi-temporal images typically either rely on comparing mono-temporal building detection outputs or requiring examples of new and removed buildings for training. This study presents a novel method using self-supervised learning principles to train a distinct object-change scoring network. It repurposes segments of the (potentially imperfect) delineations used in single-temporal detector training, harnesses bi-temporal data attributes, and leverages the assumption that most buildings remain unchanged over time. This eliminates the need for explicit examples of new or removed buildings, while still overcome usual constraints of post-detection outputmask comparison methods. We provide precisionrecall curves and examples demonstrating the improved performance of the suggested approach. Furthermore, we discuss several immediate algorithmic variations that hold the potential for even further enhancements in performance.

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