CalibMask: Addressing Class Head Miscalibration in Mask Transformers for Cross-Domain Building Footprint Extraction from Satellite Imagery

Building footprint extraction from satellite imagery is essential for urban planning, population estimation, and disaster response. While Mask Transformers such as Mask2Former have achieved state-of-the-art results on standard benchmarks, their deployment for global-scale building detection reveals a previously undocumented failure mode: class head miscalibration. We discover that when Mask2Former is trained with semantic-level labels rather than instance-level labels, the class prediction head becomes severely miscalibrated, producing maximum building confidence scores of only 8.5% despite accurate spatial predictions with 67% coverage. This miscalibration stems from the degeneration of Hungarian matching when semantic labels reduce all building instances to a single ground-truth object, creating an extreme 199:1 negative-to-positive class ratio. The result: standard post-processing produces zero detections (0% IoU). We propose CalibMask, a calibration-aware training framework that resolves this through automatic instance label generation via connected component analysis, calibrated weight transfer from a properly-trained regional model, and differential learning rate scheduling. Trained on 17,305 tiles spanning 9 countries, CalibMask achieves 50.1% IoU on USA, 49.6% on UK, and 53.8% on France (zero-shot), while restoring standard prediction to full functionality. A comprehensive ablation study confirms that instance labels are the most critical component (44% relative IoU drop without them), followed by calibrated transfer (19%).