从太空视角：利用EOFMs导航仪器差异
The View From Space: Navigating Instrumentation Differences with EOFMs
AI总结：
本研究通过分析地球观测基础模型（EOFMs）对传感器架构的敏感性，揭示了当前模型设计的缺陷，并为模型开发者、用户和遥感科学社区指明了前进方向。
摘要：
地球观测基础模型（EOFMs）作为处理大量遥感及其他地球观测数据、并对许多关键地球监测任务产生影响的工具，其普及程度急剧上升。一个新兴趋势是利用预训练模型的输出作为“嵌入”，这些嵌入总结了高维数据，可用于通用任务，如相似性搜索和内容特定查询。然而，大多数EOFMs仅在单一模态数据上训练，然后通过匹配不同模态的波段进行应用或基准测试。现有工作尚不清楚多样化的传感器架构如何影响当前EOFMs套件的内部表示。我们在本工作中表明，EOFMs的表示空间对传感器架构高度敏感，理解这一差异为我们提供了关于当前EOFMs设计陷阱的关键视角，并指明了作为模型开发者、用户以及以稳健遥感科学为指导的社区应如何前进的方向。
英文摘要：
Earth Observation Foundation Models (EOFMs) have exploded in prevalence as tools for processing the massive volumes of remotely sensed and other earth observation data, and for delivering impact on the many essential earth monitoring tasks. An emerging trend posits using the outputs of pre-trained models as 'embeddings' which summarize high dimensional data to be used for generic tasks such as similarity search and content-specific queries. However, most EOFM models are trained only on single modalities of data and then applied or benchmarked by matching bands across different modalities. It is not clear from existing work what impact diverse sensor architectures have on the internal representations of the present suite of EOFMs. We show in this work that the representation space of EOFMs is highly sensitive to sensor architecture and that understanding this difference gives a vital perspective on the pitfalls of current EOFM design and signals for how to move forward as model developers, users, and a community guided by robust remote-sensing science.
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