Abstract. Accurate building change detection depends on how well building boundaries are delineated, as distortions and merging errors hinder reliable correspondence. In dense urban areas, deep learning models frequently merge adjacent buildings—especially within narrow gaps—producing structural inconsistencies that lead to change detection errors. We propose a post-processing method integrating Local Rank-Based Prior Calibration, which reinterprets Softmax probabilities as percentile-based local ranks, with Graph-Cut refinement for structural correction. The refined mask is matched with historical building data to classify four change types. Experiments using aerial imagery from Seoul show that the method reduces structural errors, lowering under-segmentation from 51.64% to 22.02% and improving Intersection over Union (IoU) from 0.748 to 0.759. In change detection, it increases the mean F1-score from 0.522 to 0.608 and improves all classes, including new construction, whose F1-score rises from 0.269 to 0.707. Ablation studies confirm that calibration and graph-based refinement both contribute to the improvements. These results show that stabilizing segmentation outputs enhances the reliability of building-level change detection in dense urban environments.
Lee et al. (Fri,) studied this question.