AI RESEARCH
Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning
arXiv CS.AI
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ArXi:2604.18715v1 Announce Type: cross Earth observation foundation models encode land surface information into dense embedding vectors, yet the geometric structure of these representations and its implications for downstream reasoning remain underexplored. We characterize the manifold geometry of Google AlphaEarth's 64-dimensional embeddings across 12.1M Continental United States samples (2017--2023) and develop an agentic system that leverages this geometric understanding for environmental reasoning.