AlphaEarth Foundations

AEF combines over 3 billion observations from 10 geospatial data sources, including Sentinel-1/2, Landsat, GEDI, GRACE, and Wikipedia, to generate 64-byte embeddings via a temporal bottleneck architecture. It supports continuous-time modeling with time-conditional summarization and decoding, including for previously unseen time intervals.

AEF embeddings consistently outperform prior state-of-the-art across 15 evaluation tasks spanning thematic mapping, biophysical variable estimation, and change detection. In the max-trial setting, AEF reduces error magnitude by 23.9% on average compared to the best prior methods, with gains also holding in 1-shot and 10-shot regimes.

The model architecture leverages a Space-Time-Precision (STP) encoder combining spatial self-attention, time-axial attention, and convolutional precision blocks. A variational bottleneck modeled as von Mises-Fisher distributions enforces spatial precision and smooth embedding manifolds.

Evaluations include detailed benchmarks like US tree genus classification (39 classes), evapotranspiration regression, crop type mapping, and land use change detection. AEF was the only method to explain evapotranspiration (R² = 0.58), and it achieved >78% accuracy on supervised change detection tasks.

Ablations show that increasing both the number and diversity of input sources improves performance, with diminishing returns past radar or environmental data. AEF also maintains performance under aggressive 8-bit quantization, enabling efficient storage and deployment.