做地理空间AI或遥感应用的团队，别再只盯着单个模型刷榜——这篇告诉你融合多个Earth embedding模型能带来实际性能提升，建议直接参考其互补性评估方法。
Earth embedding模型将地球观测数据转化为与地理位置相关的嵌入向量，但现有评估通常孤立比较单个模型。本文提出嵌入互补性指数，衡量融合多个模型嵌入后的性能提升。在六个下游任务中，融合四个模型（AlphaEarth、Tessera、GeoCLIP、SatCLIP）在四个任务上优于最佳单一模型。互补性因任务和地点而异，且部分由土地覆盖类别的空间尺度决定。研究重新定义了Earth embedding的评估方式：未来最大收益可能来自模型组合而非单一模型。
Better Together: Evaluating the Complementarity of Earth Embedding Models
Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically evaluated in isolation, comparing the downstream task performance across different Earth embeddings. However, spatially aligned embeddings can naturally be fused, providing richer information per location, a capability that isolated evaluations fail to capture. We therefore propose assessing Earth embeddings by their complementarity: the performance gain of fused embeddings over the best single-model baseline. To operationalise this, we introduce an embedding complementarity index applicable to any embedding and task, and evaluate four Earth embedding models (AlphaEarth, Tessera, GeoCLIP, SatCLIP) in isolation, in all pairs, and jointly across six downstream tasks. Fused embeddings outperform the best single model in four out of six tasks, confirming that single-embedding evaluations often underestimate Earth embedding capabilities. Complementarity proves both task- and location-dependent. Further, for a land cover regression task, we find that complementarity is partially determined by the spatial scale of land cover classes. Complementarity reframes Earth embeddings: the greatest future gains may come not from any single Earth embedding model, but from combinations that are better together.