发表机构
University of Waterloo; University of Cambridge; Aalto University（滑铁卢大学; 剑桥大学; 阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估三种地球观测基础模型嵌入中可恢复的地理信息，发现均含可预测坐标，其中AlphaEarth表现最佳，并提出将地理信息内容作为审计EO模型的额外标准。
AI 中文摘要
地球观测（EO）基础模型提供可复用的嵌入，然而下游任务的准确性并不能揭示这些表示是否编码了地理信息，这些信息可能对位置感知应用有益，但在需要与地理位置无关的表示时可能有害。因此，我们通过测试是否可以从嵌入表示中预测坐标来评估Tessera v1、Tessera v1.1和AlphaEarth的地理坐标鲁棒性，使用了2024年284个质量验证的欧洲太阳能农场。我们通过余弦距离与测地距离之间的关联以及EPSG:3035中投影坐标的预测来评估地理信息内容。所有三个EO基础模型的嵌入都包含可恢复的地理信息。所有预测模型均显著优于训练范围均匀随机采样基线，其中AlphaEarth表现出最强的距离关联和最低的平均测地误差。两个Tessera变体也产生了比Sentinel-2对照组更高的地理距离相关性。这些发现促使地理信息内容作为审计EO基础模型的额外标准。
英文摘要
Earth-observation (EO) foundation models provide reusable embeddings, yet downstream task accuracy does not reveal whether these representations encode geographic information, which may be beneficial for location-aware applications but potentially detrimental when representations invariant to geographic location are desired. We therefore evaluate the geographic coordinate robustness of Tessera v1, Tessera v1.1, and AlphaEarth by testing whether coordinates can be predicted from the embedding representations using 284 quality-verified European solar farms from 2024. We assessed geographic information content information through the association between cosine and geodesic distances and through prediction of projected coordinates in EPSG:3035. Embeddings from all three EO foundation models contain recoverable geographic information. All prediction models significantly outperform training-range uniform random sampling baselines, with AlphaEarth exhibiting the strongest distance association and lowest mean geodesic error. Both Tessera variants also yielded higher geographic distance correlations than the Sentinel-2 controls. These findings motivate geographic information content as an additional criterion for auditing EO foundation models.
