NeurEO: dissecting Earth observation embeddings with computational neuroscience

Thijs L van der Plas; Jacob JW Bakermans; Ioannis N Athanasiadis

cs.CVOct 29, 2026

NeurEO: dissecting Earth observation embeddings with computational neuroscience

Authors: Thijs L van der Plas, Jacob JW Bakermans, Ioannis N Athanasiadis

Organizations: Wageningen University & Research, NL · University College London, UK

Abstract

Earth embeddings are compressed representations of rich Earth observation data that enable downstream models to tackle a wide range of complex geospatial tasks. Yet, this increase in performance comes at the cost of decreased interpretability, as the semantic meaning of Earth embeddings is unknown. To address this challenge, we borrow methods from the field of computational neuroscience, which aim to interpret activity in (biological) neurons. Here, we adapt and apply these methods to assign meaning to AlphaEarth embeddings. We analyse embedding features both as stand-alone data, equivalent to spontaneous neural activity, and in relation to land cover and elevation correlates, equivalent to stimulus-response neural activity. We demonstrate correlational and multi-scale structure in the embedding features. Embedding features can display highly specific and directional tuning to land cover and elevation, such as a `North vs South woodland edge' response. When we remove the main trends of combined land cover responses (like water versus land tuning), we discover fine-grained representational details with increased effective dimensionality. Together, our analyses start to unravel what these Earth embeddings represent, which is critical for developing trustworthy downstream applications.

Figures & tables

Figure 1: Embeddings are characterised by their statistical properties. a) Features exhibit a clustered (Pearson) correlation structure, sorted by hierarchical clustering (left). Median effective ranges (across patches) were calculated per feature (right). b) Effective range distributions vary slightly per feature (sorted in this panel only by median effective range). c) Variation in effective range is not correlated to variation in feature correlation structure (Pearson’s $\rho=0.026$ , two-sided $p=0.082$ , permutation test with $N=10,000$ ). d) Three example feature embedding maps for one example patch (F: feature, R: effective range for this patch). e) The effective dimensionality $D$ of features increases with increasing number of patches $N$ (mean $\pm$ standard error of the mean).

Figure 2: Embedding tuning surfaces reveal directional coding. a) Tuning surfaces (TSs) are computed by averaging hypothesis windows weighed by the focal embedding value. b) TSs of embedding feature 47, with omnidirectional coding for water and directional coding for trees. c) Embedding feature maps of feature 47 show positive coding for water bodies and positive/negative coding for Northern/Southern woodland edges. d) TS fit statistics for all feature/hypothesis combinations.

Figure 3: Embeddings can be linearly split into low-dimensional reconstructions and high-dimensional residuals. a) RGB, embedding feature maps, reconstructions and residuals for hypotheses (H) and CCA for an example embedding feature with $R^{2}_{H}=0.72$ and $R^{2}_{CCA}=0.88$ . b) CCA reconstructions explain more embedding variance ( $R^{2}$ ) than hypotheses reconstructions. c) PCA dimensionality (dim.) is greatest for residuals, followed by embeddings and reconstructions.

Geospatial Foundation Models

Geospatial foundation models turn satellite, map, and Earth-system data into reusable representations for spatial analysis and prediction.

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Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · KTH Royal Institute of Technology, Stockholm, Sweden +1
Better Together: Evaluating the Complementarity of Earth Embedding Models
May 18, 2026Thijs L van der Plas, Jacob JW Bakermans, Vishal Nedungadi +3Geospatial Foundation Models Geospatial Representation Learning
Wageningen University, NL · University College London, UK · University of Bonn, Germany
Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning
Apr 20, 2026Mashrekur Rahman, Samuel J. Barrett, Christina LastGeospatial Foundation Models Geospatial Reasoning
Dartmouth Libraries, Dartmouth College, Hanover, 03755, NH, USA. · LGND AI, Canarias, Spain. · TipplyAI, London, UK.

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Earth Embeddings

Adam J. Stewart, Heng Fang, Isaac A. Corley, Xiao Xiang Zhu

Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · KTH Royal Institute of Technology, Stockholm, Sweden+1

Geospatial Representation Learning Land Cover Classification Remote Sensing Earth Science

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Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to download and process the imagery used to generate them. Earth embeddings are vectors that summarize locations, image patches, or pixels, letting users analyze compact features instead of repeatedly training or running large models on raw satellite imagery. This chapter explains the main types of Earth embeddings, from implicit location encoders to explicit patch and pixel products, and compares their coverage, resolution, dimensionality, storage cost, licenses, and reproducibility. We review their use in land cover and crop mapping, ecological and hazard modeling, socioeconomic prediction, and semantic search, with evidence on when embeddings improve on conventional features and when pooling, fusion, or spatial transfer limit performance. Two case studies show practical workflows for similarity search and land cover mapping. We close with guidance for choosing, evaluating, storing, compressing, and publishing embeddings, and with open problems in oceanic and atmospheric coverage, uncertainty, and benchmarking.

Abstract

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