用稀疏标签生成全球高精度地图，效率远超传统方法。
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
- 基于嵌入场模型融合时空与测量信息，实现跨源数据统一表征。
- 在多种地图评估任务中表现优于现有主流特征化方法，无需重训练。
- 适合需要快速构建全球监测系统的科研与应用团队。
全球持续产生海量地球观测数据，但高质量标注仍因实地测量耗时耗力而稀缺。为此，我们提出AlphaEarth Foundations——一种嵌入场模型，可整合多源数据的时空与测量上下文，实现从局部到全球尺度的精准高效制图与监测系统构建。该模型生成的嵌入表示在多样化地图评估中，始终优于其他广泛采用的特征化方法，且无需重新训练。我们已发布2017至2024年全球、年度、分析就绪的嵌入场图层数据集。
原文摘要 · Abstract (English)
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.
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