AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
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
全球范围内持续收集的地球观测数据量空前庞大，但由于需要实地测量和观测，高质量标签仍然稀缺。这导致了对定制化建模工作的巨大投入，旨在将稀疏标签转化为地图。本文介绍AlphaEarth Foundations，这是一种嵌入场模型，能够生成高度通用的地理空间表征，整合来自多源的空间、时间和测量上下文信息，实现从局部到全球尺度的精确高效地图和监测系统生产。AlphaEarth Foundations生成的嵌入是唯一能够在多样化制图评估中始终优于所有先前测试的特征化方法而无需重新训练的模型。我们将发布2017至2024年全球年度分析就绪的嵌入场图层数据集。
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 mul...