针对韩城城市建筑变化检测难题，提出兼顾外观鲁棒与边界精准的专用网络。
UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring
- 设计专用双分支卷积网络，融合多线索比对与边界监督机制
- 在稀疏变化和强光照差异场景下F1提升至0.6175与0.7285
- 适合需高精度建筑轮廓变化监测的智慧城市管理场景
从双时相航拍影像中进行城市建筑变化检测对于再开发监控、基础设施管理和非法建设筛查至关重要，但韩国城市场景仍具挑战：变化区域稀疏，不同时间采集的图像外观差异大，且输出需严格遵循建筑轮廓而非粗糙区域。本文提出UrbanCDNet，一种任务专用的双分支卷积神经网络，结合外观鲁棒的多线索对比、对齐感知的中尺度差分、轻量级上下文细化、场景校准及辅助边界监督。实验基于修正后的AIHub韩国基准数据集，包含3,998对训练、503对验证和499对测试样本，评估变更类的精确率、召回率、F1和IoU。在锁定测试集上，UrbanCDNet实现0.7335精确率、0.7696召回率、0.7511 F1和0.6014 IoU，优于强基线Siamese U-Net（0.7108 F1，0.5514 IoU）和最强外部竞争者ChangeFormer-MIT-B0（0.7107 F1，0.5512 IoU）。诊断切片显示，在变化面积小于5%的稀疏变化子集上F1由0.4765升至0.6175，在高光度差异子集上由0.6349升至0.7285；3像素容忍度下的边界F1由0.3445升至0.4447，IoU=0.3时的物体F1由0.0690升至0.2258。结果表明，在该韩国基准上，任务定制化时序对比与边界感知监督的重要性超过模型规模本身。
原文摘要 · Abstract (English)
Urban building change detection from bi-temporal aerial imagery is important for redevelopment monitoring, infrastructure management, and unauthorized-construction screening, but Korean urban scenes remain difficult because changed regions are often sparse, appearance varies strongly between acquisition dates, and useful outputs must follow building footprints rather than coarse blobs. This paper presents UrbanCDNet, a task specific Siamese CNN that combines appearance-robust multi-cue comparison, alignment-aware middle-scale differencing, lightweight context refinement, scene calibration, and auxiliary boundary supervision. Experiments use a corrected AIHub-based Korean benchmark with 3,998 training, 503 validation, and 499 test pairs, and report changed-class precision, recall, F1, and IoU. On the locked test split, UrbanCDNet achieves 0.7335 precision, 0.7696 recall, 0.7511 F1, and 0.6014 IoU, outperforming a strong Siamese U-Net baseline (0.7108 F1, 0.5514 IoU) and the strongest external competitor, ChangeFormer-MIT-B0 (0.7107 F1, 0.5512 IoU). Additional diagnostic slicing shows that the gain is concentrated in the operating regimes that motivated the design: on the sparse-change subset with less than 5% changed area, F1 improves from 0.4765 to 0.6175, and on the high photometric-gap subset it improves from 0.6349 to 0.7285. Boundary F1 at 3-pixel tolerance rises from 0.3445 to 0.4447, while object F1 at IoU 0.3 rises from 0.0690 to 0.2258. These results indicate that, on this Korean benchmark, task-shaped temporal comparison and boundary-aware supervision matter more than generic model scale alone
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