Earth Embeddings Reveal Diverse Urban Signals from Space
Wenjing Gong, Udbhav Srivastava, Yuchen Wang, Yuhao Jia, Qifan Wu, Weishan Bai, Yifan Yang, Xiao Huang, Xinyue Ye

TL;DR
This study benchmarks Earth embeddings from satellite images for predicting diverse urban indicators across U.S. cities, revealing their strengths and limitations for scalable, neighborhood-level urban monitoring.
Contribution
It introduces a unified framework to evaluate Earth embeddings for urban signal prediction, highlighting their effectiveness and challenges in capturing various city-level indicators.
Findings
Earth embeddings predict built-environment-related outcomes well.
Indicators influenced by fine-scale behavior are harder to infer.
Representation efficiency impacts predictive performance significantly.
Abstract
Conventional urban indicators derived from censuses, surveys, and administrative records are often costly, spatially inconsistent, and slow to update. Recent geospatial foundation models enable Earth embeddings, compact satellite image representations transferable across downstream tasks, but their utility for neighborhood-scale urban monitoring remains unclear. Here, we benchmark three Earth embedding families, AlphaEarth, Prithvi, and Clay, for urban signal prediction across six U.S. metropolitan areas from 2020 to 2023. Using a unified supervised-learning framework, we predict 14 neighborhood-level indicators spanning crime, income, health, and travel behavior, and evaluate performance under four settings: global, city-wise, year-wise, and city-year. Results show that Earth embeddings capture substantial urban variation, with the highest predictive skill for outcomes more directly…
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