Uncertainty-Based Quality-Control Prioritization for High-Resolution Land-Cover Mapping Under Spatially Disjoint Evaluation

Conventional map-accuracy metrics do not indicate where limited quality-control effort should be allocated. This study evaluates uncertainty-based ranking for post-mapping quality control of selected 25 cm RGB land-cover image tiles under spatially disjoint testing. The dataset contained 15,535 tiles grouped by 359 map sheets in three regions of the Republic of Korea; 12,613 tiles were used to train U-Net, DeepLabV3+, SegFormer, and Mask2Former. SegFormer achieved the highest numerical all-valid-pixel mIoU, 0.7066 (95% confidence interval: 0.686–0.724), and a ground-truth-foreground-restricted mIoU of 0.8163. Deterministic Max-Softmax yielded an error-detection AUROC of 0.8513, compared with 0.8504 for 20-pass MC-mean Max-Softmax. In a retrospective analysis, mean BALD captured 32.9% of errors at a 20% reference-foreground-normalized budget that requires reference labels. In the separate prediction-based evaluation, prioritizing 20% of the queue without reference labels captured 17.61% of the reported errors, below the 20.05% random-order mean. These percentages use different budget definitions and are not a matched operational comparison. Pixelwise ranking therefore does not establish an advantage for the evaluated tile-level prioritization, and full-sheet performance, labor savings, and generalization to unseen regions remain unverified.