AlphaEarth Satellite Embeddings for Modelling Climate Sensitive Diseases Towards Global Health Resilience
Usman Nazir, I-Han Cheng, Sara Khalid

TL;DR
This paper evaluates the utility of AlphaEarth satellite embeddings as predictors for climate-sensitive health outcomes like malaria, respiratory infections, and stunting, demonstrating their predictive value at certain spatial scales.
Contribution
It introduces the use of 64-dimensional satellite embeddings for modeling health outcomes, providing empirical evidence of their predictive utility across multiple diseases and countries.
Findings
Embeddings improve malaria prediction in Nigeria.
Predict respiratory infections with increased R^2 across 11 countries.
Satellite data's predictive power for stunting is limited at country level.
Abstract
Malaria, childhood acute respiratory infection, and child undernutrition together account for over two million deaths annually in children under five, with the burden concentrated in low and middle-income countries where climate variability modulates transmission, exposure, and nutritional outcomes. Routine health surveillance in these settings remains sparse and reactive. Satellite-derived representations of the Earth's surface offer a scalable, low-cost complement to traditional covariates, yet their utility as predictors of population health outcomes is poorly characterised. We summarise findings from three studies evaluating AlphaEarth Foundations 64-dimensional satellite embeddings as predictors of population health outcomes, focusing on vulnerable populations. The studies span infectious disease (malaria, respiratory infection) and stunting. In each study, embeddings provide…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
