# Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts

arXiv: https://arxiv.org/abs/2511.10300 · HTML: https://mlsanity.com/paper/2511.10300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Towards Stable Test-Time Adaptation in Dynamic Wild World](https://mlsanity.com/paper/2302.12400/) (2023)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift](https://mlsanity.com/paper/2006.10963/) (2020)
- [Improving Unsupervised Image Clustering With Robust Learning](https://mlsanity.com/paper/2012.11150/) (2020)
- [UV-SAM: Adapting Segment Anything Model for Urban Village Identification](https://mlsanity.com/paper/2401.08083/) (2024)
- [Lightweight and Robust Representation of Economic Scales from Satellite Imagery](https://mlsanity.com/paper/1912.08197/) (2019)
- [Learning Economic Indicators by Aggregating Multi-Level Geospatial Information](https://mlsanity.com/paper/2205.01472/) (2022)
- [Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data](https://mlsanity.com/paper/1901.00861/) (2019)
- [Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach](https://mlsanity.com/paper/2204.04419/) (2022)
- [Slum Segmentation and Change Detection : A Deep Learning Approach](https://mlsanity.com/paper/1811.07896/) (2018)

## Built on by (most cited first, 6 of 6)

- [EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images](https://mlsanity.com/paper/2603.29441/) (2026)
- [GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards](https://mlsanity.com/paper/2605.20006/) (2026)
- [A high-resolution nationwide urban village mapping product for 342 Chinese cities based on foundation models](https://mlsanity.com/paper/2602.18765/) (2026)
- [The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?](https://mlsanity.com/paper/2603.20305/) (2026)
- [Mixture-of-Experts in Remote Sensing: A Survey](https://mlsanity.com/paper/2604.03342/) (2026)
- [Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C'ordoba, Argentina](https://mlsanity.com/paper/2608.28680/) (2026)

## Abstract (arXiv)

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.