揭示遥感传感器差异如何影响模型表征，为改进地球观测模型设计提供关键视角。
The View From Space: Navigating Instrumentation Differences with EOFMs
- 分析不同遥感传感器对预训练模型内部表征的影响机制
- 发现模型表征空间对传感器架构高度敏感，存在显著差异
- 提醒开发者和用户警惕跨传感器应用的潜在偏差，适合遥感建模研究者
地球观测基础模型（EOFMs）在处理海量遥感数据、支持各类地球监测任务方面日益普及。当前趋势是将预训练模型输出作为‘嵌入’，用于相似性搜索与内容查询等通用任务。然而，多数EOFM仅基于单一模态数据训练，实际应用中常通过波段匹配跨模态使用，但现有研究未阐明不同传感器架构对模型内部表示的具体影响。本文表明，EOFM的表征空间对传感器架构极为敏感，理解这一差异有助于揭示当前模型设计的局限性，并为未来模型开发、应用及以稳健遥感科学为导向的研究提供重要指引。
原文摘要 · Abstract (English)
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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