Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping

• URL: http://arxiv.org/abs/2511.01408v1
• Date: Mon, 03 Nov 2025 10:00:31 GMT
• Title: Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping
• Authors: Markus B. Pettersson, Adel Daoud,
• Abstract summary: We propose a graph-based approach to predict cluster-level wealth indices across Sub-Saharan Africa.<n>By modeling spatial relations between surveyed and unlabeled locations, we improve the generalization of wealth predictions beyond existing surveys.
• Score: 2.913413516711343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate, fine-grained poverty maps remain scarce across much of the Global South. While Demographic and Health Surveys (DHS) provide high-quality socioeconomic data, their spatial coverage is limited and reported coordinates are randomly displaced for privacy, further reducing their quality. We propose a graph-based approach leveraging low-dimensional AlphaEarth satellite embeddings to predict cluster-level wealth indices across Sub-Saharan Africa. By modeling spatial relations between surveyed and unlabeled locations, and by introducing a probabilistic "fuzzy label" loss to account for coordinate displacement, we improve the generalization of wealth predictions beyond existing surveys. Our experiments on 37 DHS datasets (2017-2023) show that incorporating graph structure slightly improves accuracy compared to "image-only" baselines, demonstrating the potential of compact EO embeddings for large-scale socioeconomic mapping.

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