Earth science in the era of foundation models: How AlphaEarth is reshaping quantitative geoscience

doi:10.13745/j.esf.sf.2025.9.68

Abstract

Abstract:

Since the beginning of the 21st century, advances in big data and artificial intelligence have driven a paradigm shift in the geosciences, moving the field from qualitative descriptions toward quantitative analysis, from observing phenomena to uncovering underlying mechanisms, from regional-scale investigations to global perspectives, and from experience-based inference toward data- and model-enabled intelligent prediction. AlphaEarth Foundations (AEF) is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space, enabling—for the first time—standardized representation and seamless integration of 12 distinct types of Earth observation data, including optical, radar, and lidar. This framework significantly improves data assimilation efficiency and resolves the persistent problem of “data silos” in geoscience research. AEF is helping redefine research methodologies and fostering breakthroughs, particularly in quantitative Earth system science. This paper systematically examines how AEF’s innovative architecture—featuring multi-source data fusion, high-dimensional feature representation learning, and a scalable computational framework—facilitates intelligent, precise, and real-time data-driven geoscientific research. Using case studies from resource and environmental applications, we demonstrate AEF’s broad potential and identify emerging innovation needs. Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.

Key words: large-scale models, artificial intelligence, mineral prospectivity mapping, AlphaEarth, knowledge graphs, deep and covered mineral exploration

CLC Number:

P628
TP18

CHENG Qiuming, YANG Yilin, ZHOU Yuanzhi, ZHANG Yuanzhi. Earth science in the era of foundation models: How AlphaEarth is reshaping quantitative geoscience[J]. Earth Science Frontiers, 2025, 32(5): 1-11.

Figures/Tables 7

Fig.1 Schematic diagram of AEF and the proposed AEF+ framework. (A) AEF embedding vector matrix; (B) AEF+ extension incorporating geological (G), geophysical (Gy), and geochemical (Gc) embeddings.

Fig.2 Lithologic classification map and prediction results for the Jining Area. (A) A geology map; (B) Lithologic classification results for the Jining area obtained using the random forest method based on the AEF dataset and field-validated lithology points. The legend colors are consistent between (A) and (B). Modified after [25].

Fig.3 Lithology distribution prediction through integration of geochemical and AEF data. (A) Stream sediment geochemical data at 1∶50000 scale, showing element associations identified by principal component analysis. (B) Lithology identification results from fused geochemical and AEF data.

Fig.4 Delineation of mineralized alteration zones in the Duolong ore district, Tibet. Modified after [28]. (A) Erosion Index distribution map extracted from ASTER imagery; (B) Erosion Index distribution map derived from random forest regression using AEF data.

Fig.5 Alteration mineral classification map derived from ASTER imagery (A) and alteration mineral information distribution map extracted using a random forest multi-regression model based on AEF data (B).

Fig.6 Comprehensive surface change intensity map (Δθ, unit: degrees) around Mayon Volcano, Philippines, for 2018 relative to 2017, calculated using AEF data.

Fig.7 Estimation of river suspended sediment concentration in the Pearl River Delta using Landsat 8 and AEF data, respectively. (A) Results derived from Landsat 8 data; (B) Results derived from AEF data.

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