Decomposing Earth Embeddings with Sparse Autoencoders

Earth observation foundation models produce global meter-resolution embeddings that underpin work across agriculture, ecology, hydrology, and beyond. Yet the embeddings themselves are opaque: each dimension entangles unrelated physical concepts, rendering their interpretability and robustness challenging for practitioners and scientists alike. Here, we propose sparse autoencoders (SAEs) as a principled way to decompose Earth embeddings into overcomplete dictionaries of sparse, approximately monosemantic features. We introduce an evaluation protocol suited to a domain where LLM-based auto-interpretability fails and spatial autocorrelation matters, scoring any SAE on reconstruction, sparsity, monosemantic recovery, and spatial coherence against a curated label registry of categorical and continuous reference products. On this protocol, three state-of-the-art SAE recipes (ReLU+$\ell_1$, BatchTopK, and Matryoshka BatchTopK) all recover monosemantic and spatially contiguous concepts from AlphaEarth and Tessera embeddings much better than raw embeddings, with Matryoshka the strongest of the three. Two applications follow: (i) unsupervised discovery of land-cover subconcepts (e.g., splitting water into river, lake, ocean), and (ii) improved area-of-applicability estimates for OOD detection. Together, these results are a step toward Earth observation models whose features can be inspected rather than only used, opening pathways for scientific discovery and improved trustworthiness in geospatial applications. Code and models will be released upon publication.