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

• URL: http://arxiv.org/abs/2507.22291v1
• Date: Tue, 29 Jul 2025 23:55:00 GMT
• Title: AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
• Authors: Christopher F. Brown, Michal R. Kazmierski, Valerie J. Pasquarella, William J. Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, Noel Gorelick, Lihui Lydia Zhang, Sophia Alj, Emily Schechter, Sean Askay, Oliver Guinan, Rebecca Moore, Alexis Boukouvalas, Pushmeet Kohli,
• Abstract summary: We introduce AlphaEarth Foundations, an embedding field model yielding a highly general, geospatial representation.<n>We will release a dataset of global, annual, analysis-ready embedding field layers from 2017 through 2024.
• Score: 18.7927140265097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 all previous featurization approaches tested on a diverse set of mapping evaluations without re-training. We will release a dataset of global, annual, analysis-ready embedding field layers from 2017 through 2024.

Related papers

• HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation [1.0408909053766147]
  We introduce a globally distributed benchmark dataset for forest aboveground biomass (AGB) estimation.<n>This benchmark dataset combines co-located hyperspectral imagery (HSI) from the Environmental Mapping and Analysis Program (EnMAP) satellite and predictions of AGB density estimates.<n>Our experimental results on this dataset demonstrate that the evaluated Geo-FMs can match or, in some cases, surpass the performance of a baseline U-Net.
  arXiv  Detail & Related papers  (2025-06-12T21:29:20Z)
• TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation [65.74990259650984]
  We introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery.<n>Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism.<n>TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
  arXiv  Detail & Related papers  (2025-06-06T17:59:50Z)
• Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping [2.9212099078191756]
  BRIDGE-LC is a novel deep learning framework that integrates multi-scale geospatial information into the land cover classification process.<n>Our results demonstrate that integrating geospatial information improves land cover mapping performance.
  arXiv  Detail & Related papers  (2025-04-16T17:42:46Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
  This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.<n>The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.<n>Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
  arXiv  Detail & Related papers  (2025-01-14T13:42:22Z)
• Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space [0.0]
  There is a growing need for efficient vector representations of the underlying raw data.<n>The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data.<n>In this work, an extension is proposed to an existing community project, Major TOM, focused on the provision and standardization of open and free AI-ready datasets for Earth observation.
  arXiv  Detail & Related papers  (2024-12-07T09:49:47Z)
• SpectralEarth: Training Hyperspectral Foundation Models at Scale [47.93167977587301]
  We introduce SpectralEarth, a large-scale multi-temporal dataset designed to pretrain hyperspectral foundation models. We pretrain a series of foundation models on SpectralEarth using state-of-the-art self-supervised learning (SSL) algorithms. We construct four downstream datasets for land-cover and crop-type mapping, providing benchmarks for model evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:55:59Z)
• Ben-ge: Extending BigEarthNet with Geographical and Environmental Data [1.1377027568901037]
  We present the ben-ge dataset, which supplements the BigEarthNet-MM dataset by compiling freely and globally available geographical and environmental data. Based on this dataset, we showcase the value of combining different data modalities for the downstream tasks of patch-based land-use/land-cover classification and land-use/land-cover segmentation.
  arXiv  Detail & Related papers  (2023-07-04T14:17:54Z)
• OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping [15.419052489797775]
  OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. It consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents.
  arXiv  Detail & Related papers  (2022-10-19T17:20:16Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [69.47585818994959]
  We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. We utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas.
  arXiv  Detail & Related papers  (2022-01-31T20:02:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.