RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction
Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmental monitoring. However, obtaining dense pixel-level annotations is costly, motivating the use of semi-supervised learning (SSL) to leverage unlabeled imagery. In remote sensing, severe foreground--background imbalance poses a particular challenge for self-training, as it can bias pseudo-label generation and the resulting unsupervised optimization toward the majority background class. We show that addressing this imbalance at only one stage is insufficient: balancing pseudo-label selection alone does not prevent background bias from re-emergi...
| Authors | Akil Ahmad Taki, Shaikh Anowarul Fattah |
|---|---|
| Primary category | cs.CV |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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