行星特征场是可扩展的地球表示
Planetary Feature Fields are Scalable Earth Representations
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中文总结 AI 辅助
针对多源地球观测数据冗余存储问题，提出行星特征场（PFFs），以共享因子化特征体积和轻量解码器联合建模时空连续场，实现1800倍压缩下保留90%以上特征性能，并显著降低访问延迟。
中文摘要 AI 辅助
卫星观测、预计算嵌入和地图产品描述了同一不断演变的地球，却以独立的、PB级数据产品形式存储。它们的持续增长要求对多种产品进行紧凑表示，同时保留空间和时间细节。我们引入行星特征场（PFFs），通过将多种产品联合建模为行星尺度上空间和时间的连续函数，利用数据产品间的冗余性。PFFs是空间局部的显式-隐式（混合）神经场。每个场共享一个因子化特征体积——将显式3D网格分解为更小因子——跨产品共享，而轻量级隐式解码器在多个时间步上重建单个产品。在匹配压缩率下，PFFs在空间和时间上重建EO产品的准确性优于单产品场。相对于未压缩源数据，在$1800\ imes$压缩下，重建特征在像素级分割、变化检测和补丁级分类任务上保留了原始特征性能的约$90\%$或更多。PFFs可通过扩展其因子化特征体积添加新时间步，并通过附加新解码器添加新产品，同时保持现有输出不变。相对于评估的API和云存储管道，PFFs将端到端特征访问延迟降低了一个数量级。
英文摘要
Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data products. Their continued growth calls for compact representations of multiple products while preserving spatial and temporal detail. We introduce Planetary Feature Fields (PFFs), which exploit redundancy across data products by modeling them jointly as continuous functions of space and time at planetary scale. PFFs are spatially local explicit-implicit (hybrid) neural fields. Each field shares a factored feature volume---a decomposition of an explicit 3D grid with smaller factors---across products, while lightweight implicit decoders reconstruct individual products across multiple timesteps. PFFs reconstruct EO products over space and time more accurately than single-product fields at matched compression rates. At $1800\times$ compression relative to the uncompressed source data, reconstructed features retain approximately $90\%$ or more of the performance achieved with the original features on pixel-level segmentation, change detection, and patch-level classification tasks. PFFs can add new timesteps by extending their factored feature volumes and add new products by attaching new decoders, while leaving existing outputs unchanged. PFFs reduce end-to-end feature access latency by an order of magnitude relative to evaluated API and cloud-storage pipelines.
发表机构
- University of British Columbia（不列颠哥伦比亚大学）
- University of Copenhagen（哥本哈根大学）
- Carleton University（卡尔顿大学）
- Vector Institute（向量研究所）
- Taylor Geospatial（泰勒地理空间研究所）
机构由 AI 辅助整理，请以论文原文为准。
