{"ID":2842273,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10300","arxiv_id":"2511.10300","title":"Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.10300","url_pdf":"https://arxiv.org/pdf/2511.10300v1","authors":"[\"Sumin Lee\",\"Sungwon Park\",\"Jeasurk Yang\",\"Jihee Kim\",\"Meeyoung Cha\"]","published":"2025-11-13T13:35:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CY\"]","methods":"[]","has_code":false}