# AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

arXiv: https://arxiv.org/abs/2507.22291 · HTML: https://mlsanity.com/paper/2507.22291/

## Key facts

- Cited by 137 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2025.

## Built on by (most cited first, 30 of 137)

- [Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents](https://mlsanity.com/paper/2509.23141/) (2025)
- [OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation](https://mlsanity.com/paper/2511.13655/) (2025)
- [No One Knows the State of the Art in Geospatial Foundation Models](https://mlsanity.com/paper/2605.12678/) (2026)
- [Measuring the Intrinsic Dimension of Earth Representations](https://mlsanity.com/paper/2511.02101/) (2025)
- [NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation](https://mlsanity.com/paper/2510.17914/) (2025)
- [Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach](https://mlsanity.com/paper/2512.20056/) (2025)
- [Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence](https://mlsanity.com/paper/2602.10354/) (2026)
- [Cropland Mapping using Geospatial Embeddings](https://mlsanity.com/paper/2511.02923/) (2025)
- [Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access](https://mlsanity.com/paper/2601.13134/) (2026)
- [PRUE: A Practical Recipe for Field Boundary Segmentation at Scale](https://mlsanity.com/paper/2603.27101/) (2026)
- [CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities](https://mlsanity.com/paper/2605.26036/) (2026)
- [UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation](https://mlsanity.com/paper/2606.23503/) (2026)
- [TESSERA v2: Scaling Pixel-wise Earth Foundation Models](https://mlsanity.com/paper/2607.03949/) (2026)
- [Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring](https://mlsanity.com/paper/2601.11183/) (2026)
- [Embedding -based Crop Type Classification in the Groundnut Basin of Senegal](https://mlsanity.com/paper/2601.16900/) (2026)
- [Inferring Height from Earth Embeddings: First insights using Google AlphaEarth](https://mlsanity.com/paper/2602.17250/) (2026)
- [Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand](https://mlsanity.com/paper/2602.23678/) (2026)
- [From Pixels to Patches: Pooling Strategies for Earth Embeddings](https://mlsanity.com/paper/2603.02080/) (2026)
- [What on Earth is AlphaEarth? Hierarchical structure and functional interpretability for global land cover](https://mlsanity.com/paper/2603.16911/) (2026)
- [Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning](https://mlsanity.com/paper/2604.18715/) (2026)
- [FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensing](https://mlsanity.com/paper/2509.23927/) (2025)
- [Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2510.22964/) (2025)
- [Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping](https://mlsanity.com/paper/2511.01408/) (2025)
- [SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation](https://mlsanity.com/paper/2511.10370/) (2025)
- [Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings](https://mlsanity.com/paper/2601.01558/) (2026)
- [Localized, High-resolution Geographic Representations with Slepian Functions](https://mlsanity.com/paper/2602.00392/) (2026)
- [Earth Embeddings Reveal Diverse Urban Signals from Space](https://mlsanity.com/paper/2604.03456/) (2026)
- [Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis](https://mlsanity.com/paper/2605.21804/) (2026)
- [Mapping on a Budget: Optimizing Spatial Data Collection for ML](https://mlsanity.com/paper/2509.03749/) (2025)
- [Deep Learning-Based Burned Area Mapping Using Bi-Temporal Siamese Networks and AlphaEarth Foundation Datasets](https://mlsanity.com/paper/2509.07852/) (2025)

## Abstract (arXiv)

Unprecedented volumes of Earth observation data are continually collected around the world, but high-quality labels remain scarce given the effort required to make physical measurements and observations. This has led to considerable investment in bespoke modeling efforts translating sparse labels into maps. Here we introduce AlphaEarth Foundations, an embedding field model yielding a highly general, geospatial representation that assimilates spatial, temporal, and measurement contexts across multiple sources, enabling accurate and efficient production of maps and monitoring systems from local to global scales. The embeddings generated by AlphaEarth Foundations are the only to consistently outperform a suite of other well-known/widely accepted featurization approaches tested on a diverse set of mapping evaluations without re-training. We have released a dataset of global, annual, analysis-ready embedding field layers from 2017 through 2024.