Building Segmentation Refinement via Local Rank-Based Calibration and Graph Cut

Building segmentation in dense urban scenes remains challenging due to frequent undersegmentation, where adjacent buildings are erroneously merged into single objects.Conventional thresholding, morphological filtering, or context-based optimization methods such as Conditional Random Fields (CRFs) and Graph Cut alleviate this issue only partially, as soft boundaries between closely located buildings often lead to incorrect merging.To address this limitation, we propose a novel post-processing framework termed Local Rank-Based Calibration and Graph Cut for building segmentation refinement.The method introduces a percentile rank-based calibration applied to the softmax probability map before binarization.Instead of treating each pixel's absolute probability as a decision boundary, our calibration reinterprets the probability as a relative percentile within a local sliding window.Low-percentile pixels are penalized via an exponential weighting function, thereby suppressing spurious foreground responses in narrow gaps between buildings, while high-percentile pixels retain their original values.The calibrated map is subsequently refined using a Graph Cut optimization, which balances unary terms from the calibrated probabilities with pairwise smoothness terms to enforce globally consistent segmentation.Experiments were conducted on high-resolution aerial orthoimages of Suseo, Seoul, using building footprints from the National Geographic Information Institute (NGII) as ground truth.SegFormer was adopted as the baseline segmentation backbone, and multiple post-processing strategies were compared under identical conditions.Quantitative results show that our method reduces the under-segmentation rate from 28.47% (baseline) to 15.33%, while maintaining the lowest over-segmentation rate (0.49%) among all tested methods.Pixellevel metrics also improved, with Intersection over Union (IoU) reaching 0.8012 and F1-score 0.8896.Visual comparisons confirm that the proposed Local Rank-Based Calibration and Graph Cut method effectively separates adjacent buildings while preserving the continuity of individual building interiors.Sensitivity analysis further demonstrates the robustness of the method across a reasonable range of parameter values.Although the Graph Cut step increases computational cost (2 hours 28 minutes for fullscene processing without GPU acceleration), the accuracy gains are significant for applications requiring reliable building-level delineation.The modular design allows our approach to be seamlessly integrated with various backbone models and combined with other post-processing methods such as morphology or CRF.In summary, this study presents a lightweight yet effective refinement strategy that substantially improves building boundary delineation in dense urban imagery.By combining local rank-based calibration with global Graph Cut optimization, the proposed method offers a generalizable and transferable solution for enhancing building segmentation in remote sensing applications.