Rapid urban expansion has fueled the growth of informal settlements in major cities of low- and middle-income countries, with Lahore and Karachi in Pakistan and Mumbai in India serving as prominent examples. However, large-scale mapping of these settlements is severely constrained not only by the scarcity of annotations but by inherent data quality challenges, specifically high spectral ambiguity between formal and informal structures and significant annotation noise. We address this by introducing a benchmark dataset for Lahore, constructed from scratch, along with companion datasets for Karachi and Mumbai, which were derived from verified administrative boundaries, totaling approximately 900 $\text{km}^\text{2}$ of urban area. This collection is supplemented by four cities from prior literature across Sub-Saharan Africa and Latin America, with comprehensive data quality assessments provided for each city. We also propose a semi-supervised segmentation framework designed to mitigate the class imbalance and distribution mismatch inherent in standard semi-supervised learning pipelines. Our method integrates a Class-Aware Adaptive Thresholding mechanism that dynamically adjusts confidence thresholds to prevent minority class suppression, and a DINOv2-based unlabeled pool filter that removes out-of-distribution tiles prior to training to reduce covariate shift. Extensive experiments across seven cities spanning three continents, repeated over five random seeds, demonstrate gains of up to +5.9 pp mIoU over state-of-the-art semi-supervised baselines, with both components being architecture-agnostic and adding no inference overhead.

翻译：快速城市化推动了中低收入国家主要城市非正规住区的扩张，巴基斯坦拉合尔和卡拉奇以及印度孟买即为典型例证。然而，大规模制图不仅受限于标注稀缺，更面临固有数据质量挑战——特别是正规与非正规结构之间高度的光谱模糊性以及严重的标注噪声。为此，我们构建了拉合尔基准数据集（从零开始创建），并配套推出基于已验证行政边界的卡拉奇和孟买数据集，累计覆盖约900平方公里城市区域。该数据集补充了来自撒哈拉以南非洲和拉丁美洲四项既往文献中的城市数据，并为每个城市提供了全面数据质量评估。我们进一步提出半监督分割框架，旨在缓解标准半监督学习管道中固有的类别不平衡与分布失配问题。该方法集成了类别感知自适应阈值机制（动态调整置信度阈值以防止少数类抑制）和基于DINOv2的无标注池过滤器（训练前剔除分布外切片以减少协变量偏移）。跨越三大洲七座城市、基于五个随机种子的重复实验表明，该方法相较现有最优半监督基线最高可提升+5.9个百分点mIoU，且两个组件均与架构无关且不增加推理开销。