What on Earth is AlphaEarth? Hierarchical structure and functional interpretability for global land cover

Abstract

Geospatial foundation models generate high-dimensional embeddings that achieve strong predictive performance, yet their internal organization remains obscure, limiting their scientific use. Recent interpretability studies relate Google AlphaEarth Foundations (GAEF) embeddings to continuous environmental variables, but it is still unclear whether the embedding space exhibits a functional or hierarchical organization, in which some dimensions act as specialized representations while others encode shared or broader geospatial structure. In this work, we propose a functional interpretability framework that reverse-engineers the role of embedding dimensions by characterizing their contribution to land cover structure from observed classification behavior. The approach combines large-scale experimentation with a structural analysis of embedding-class relationships based on feature importance patterns and progressive ablation. Our results show that embedding dimensions exhibit consistent and non-uniform functional behavior, allowing them to be categorized along a hierarchical functional spectrum: specialist dimensions associated with specific land cover classes, low- and mid-generalist dimensions capturing shared characteristics between classes, and highgeneralist dimensions reflecting broader environmental gradients. Critically, we find that accurate land cover classification (98% of baseline performance) can be achieved using as few as 2 to 12 of the 64 available dimensions, depending on the class. This demonstrates substantial redundancy in the embedding space and offers a pathway toward significant reductions in computational cost. Together, these findings reveal that AlphaEarth embeddings are not only physically informative, but also functionally organized into a hierarchical structure, providing practical guidance for dimension selection in operational classification tasks.

Figures (8)

• Figure 1: Multi-modal fusion at scale, left) data containing the features, and right) its representation in an embedding.
• Figure 1: Embedding frequency distributions by land cover class. For each class, embedding dimensions are ranked by frequency of appearance in the top 2 most important positions across 10,000 experimental runs. Green bars indicate dimensions within the tipping point threshold; blue bars indicate remaining dimensions. Generated using the interactive Dashboard Guthrie2025.
• Figure 2: Flowchart of the methodological framework: from embeddings to functional semantics. The pipeline integrates (1) ESA WorldCover 2020 categorical labels and AEF 64-dimensional embeddings as inputs, (2) a massive experimental framework executing more than 130,000 binary classification experiments using Random Forest, Gradient Boosting, XGBoost, and LightGBM with progressive feature ablation across the top 1 to 30 embedding dimensions, (3) structural mapping via an association matrix and functional taxonomy, and (4) an interactive embedding universe visualization connecting the latent space to semantic space.
• Figure 2: Grid-based geographic performance heatmap of classification accuracy across global experiments. Each cell represents the mean classification accuracy aggregated across all experiments conducted within that geographic grid cell. Red cells indicate higher accuracy (>90%), orange cells indicate moderate accuracy (80–90%), and blue cells indicate lower accuracy (<80%). Generated using the interactive Dashboard Guthrie2025.
• Figure 3: Embedding Fingerprint plot illustrating the functional dimensional signature of each land cover class at the $98\%$ performance recovery threshold. Analogous to a genomic fingerprint, where the presence and absence of bands at specific loci encodes biological identity, each row represents the characteristic pattern of exclusive (blue) and shared (pink) embedding dimensions that together define the discriminative identity of a land cover class within the AEF embedding space. Exclusive dimensions appear in only one class's minimum subset, while shared dimensions --- encompassing low-, mid-, and high-generalists --- contribute to the classification of two or more classes. Gray positions indicate dimensions not required to achieve the $98\%$ threshold for that class. Land cover classes are ordered from top to bottom by increasing spectral complexity, quantified as the mean pixel-wise variance across Sentinel-2 spectral bands computed over 100 randomly selected images per class after standard atmospheric and radiometric corrections. Spectrally homogeneous classes such as Snow/ice and Water --- which exhibit low inter-pixel variability due to their uniform reflectance signatures --- appear at the bottom, while spectrally heterogeneous classes such as Built-up --- whose radiometric response varies substantially across rooftops, roads, and impervious surfaces --- appear at the top, reflecting the increasing number of embedding dimensions required to characterize them.