隐匿与寻觅：探究地球观测影像中的冗余性
Hide and Seek: Investigating Redundancy in Earth Observation Imagery
AI总结：
本文研究了地球观测影像的多维冗余性，发现其对领域和应用有显著影响，通过系统分析证实冗余性普遍存在，利用冗余可显著降低计算成本，提升模型效率。
摘要：
随着地球观测（EO）数据的日益丰富和计算机视觉的最新进展，机器学习在EO领域的进步迅速，产生了越来越大规模的领域专用模型。然而，这种进步可能忽略了EO数据与其他领域根本区别的重要属性。我们主张EO数据表现出多维冗余（光谱、时间、空间和语义），其对领域及其应用的影响比现有文献所反映的更为显著。为了验证这一假设，我们进行了系统性的领域特定研究，考察了这种现象在EO变化关键维度中的存在性、一致性和实际影响。我们的发现证实，EO数据的冗余性既显著又普遍存在：利用冗余可实现与基线相当的性能（约98.5%），但计算成本仅为基线的四分之一（约4倍更少的GFLOPs），在训练和推理过程中均如此。关键的是，这些收益在不同任务、地理空间位置、传感器、地面采样距离和架构设计中保持一致；这表明多维冗余是EO数据的结构性属性，而非特定实验选择的产物。这些结果为更高效、可扩展和可访问的大规模EO模型奠定了基础。
英文摘要：
The growing availability of Earth Observation (EO) data and recent advances in Computer Vision have driven rapid progress in machine learning for EO, producing domain-specific models at ever-increasing scales. Yet this progress risks overlooking fundamental properties of EO data that distinguish it from other domains. We argue that EO data exhibit a multidimensional redundancy (spectral, temporal, spatial, and semantic) which has a more pronounced impact on the domain and its applications than what current literature reflects. To validate this hypothesis, we conduct a systematic domain-specific investigation examining the existence, consistency, and practical implications of this phenomenon across key dimensions of EO variability. Our findings confirm that redundancy in EO data is both substantial and pervasive: exploiting it yields comparable performance ($\approx98.5\%$ of baseline) at a fraction of the computational cost ($\approx4\times$ fewer GFLOPs), at both training and inference. Crucially, these gains are consistent across tasks, geospatial locations, sensors, ground sampling distances, and architectural designs; suggesting that multi-faceted redundancy is a structural property of EO data rather than an artifact of specific experimental choices. These results lay the groundwork for more efficient, scalable, and accessible large-scale EO models.
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