Earth embeddings stop being a tangle of mixed dimensions: sparse autoencoders pull out nameable geographic features, though a random dictionary already earns part of the score
Earth-observation foundation models such as AlphaEarth and Tessera compress each 10-metre pixel into a dense vector whose individual dimensions often mix several land-surface concepts. Sparse autoencoders expand that vector over an overcomplete dictionary and keep only a few atoms active, yielding sparse, approximately monosemantic features. At matched sparsity, three sparse-autoencoder variants reconstruct better than PCA, ICA, NMF and raw dimensions; yet a control whose decoder is a frozen random dictionary also shows more specific features and finer spatial scales, so those two properties follow from sparsity itself, and only a learned dictionary cuts AlphaEarth's unexplained variance eightfold.