SAM-powered building footprint updating for various cities: Sparse labels meet historical data repurposing in urban monitoring

Accurate building footprint databases are fundamental for sustainable urbanization yet face persistent updating challenges due to the rapid pace of urban change. Traditional methods rely on bi-temporal image comparison for change detection, which requires a large number of new labels to retrain the model, which is costly. We propose a passive updating paradigm that eliminates the reliance on historical imagery and leverages a lightweight adaptive strategy applied to Segment Anything Model (SAM) to minimize labeling costs. Furthermore, we propose a Cross Modal Temporal Fusion (CMTF) module that combines features from historical building footprints with those from recent imagery, alleviating the burden of small-sample training. The training process utilizes a semi-supervised approach, enabling the model to learn from both labeled and unlabeled regions, with labeled regions comprising only 0.4% of the building samples. Besides, we propose the RIO dataset, a sub-meter bi-temporal building footprint update dataset for studying building changes in rapidly developing areas. In addition, this work is validated on a range of cities worldwide, including Christchurch (post-earthquake reconstruction) and Beijing-Shanghai (megacity expansion). This work advances urban building renewal by overcoming the reliance on paired historical imagery for change detection and the need for large amounts of up-to-date labels. This approach offers a scalable solution for monitoring SDG 11 (Sustainable Cities and Communities), enabling less developed countries to use free and open product data to track urban expansion patterns with only a few labels.