无需目标地标注数据，实现全球贫民窟精准识别
Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts
- 采用专家混合架构，融合区域特征与通用模式
- 在12个城市百万级数据上训练，跨区域泛化性能显著提升
- 适合资源匮乏地区，助力全球城市贫困监测
基于卫星图像的贫民窟分割在生成全球城市贫困估计方面具有重要潜力。然而，非正式住区形态差异大，导致在特定区域训练的模型难以推广至新地区。为此，我们构建了一个涵盖四大洲12个城市的百万级高分辨率卫星影像数据集，并提出GRAM（通用区域感知专家混合）框架，一种两阶段测试时自适应方法，可在无需目标区域标注数据的情况下实现鲁棒的贫民窟分割。利用该数据集，模型通过专家混合结构捕捉区域特异性特征，同时通过共享主干网络学习通用特征。在适应阶段，通过专家间预测一致性筛选出不可靠伪标签，从而有效推广至未见地区。GRAM在非洲等低资源城市中优于现有最先进方法，为全球贫民窟制图和数据驱动的城市规划提供了可扩展、标签高效解决方案。
原文摘要 · Abstract (English)
Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without requiring labeled data from target regions. We compile a million-scale satellite imagery dataset from 12 cities across four continents for source training. Using this dataset, the model employs a Mixture-of-Experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters out unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. GRAM outperforms state-of-the-art baselines in low-resource settings such as African cities, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.
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