[Submitted on 24 Sep 2026]
Title:AlphaEarth distinguishes cities but compresses urban variation
View a PDF of the paper titled AlphaEarth distinguishes cities but compresses urban variation, by Andrew Renninger
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Abstract:Cities differ in built form, land cover and development history, complicating comparison across places and time. Satellite foundation models map Earth's surface onto common numerical representations. Yet the tasks and targets used to shape them typically do not focus on cities: globally consistent labels for urban function do not exist, and many datasets - especially land cover and land use classifications - collapse the built environment into few classes. Here we audit the representation, focusing on AlphaEarth but with broader applicability to other Earth embeddings, by probing the geometry and geography of embeddings for 1,000 urban areas in 162 countries. We find that cities occupy a shifted but overlapping region on the hypersphere, 62.7° from the global mean direction, and continent and climate predict 24.3% of variation among the mean directions of urban centres in excluded countries. Inside cities, degrees of urbanisation carry 8.9% of the variation, and what they leave holds shared directions whose local orientation varies, not one universal axis of urbanisation. Retained variation is itself unequal: dispersion within urban centres is 14.1% greater per standard deviation of national development, even after adjusting for population, land area and continent. Further controls suggest cities in developing countries present less contrast in vegetation and texture, and dispersion follows that contrast: full adjustment for it leaves at most 6.4% of the gradient. Annually, a city's representation moves nearly eight times more than redrawing its own pixels explains, and contracts where the 2022 loss of Sentinel-1B removed a pass direction. AlphaEarth's representations therefore support comparison across regions, while the differences between its annual layers are not yet validated for comparison over time.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Physics and Society (physics.soc-ph)
Cite as: arXiv:2609.30356 [cs.CV]
(or arXiv:2609.30356v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30356
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Andrew Renninger [view email] [v1] Thu, 24 Sep 2026 17:43:28 UTC (9,139 KB)
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