提出按任务类型决定数据上天的框架，用语义压缩大幅减少太空传输数据量。
Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction
- 以任务为中心，通过语义抽象判断哪些工作负载适合上太空。
- 卫星影像处理实现99.7%-99.99%的数据压缩率，3D重建减少99.49%体积。
- 适合关注太空计算部署、低带宽场景下数据压缩的研究者与工程师。
随着发射成本下降和数据密集型人工智能工作负载增长，空间计算正变得可行。本文提出一种以工作负载为核心的框架，用于判断哪些任务应部署在轨道而非地面云上，并建立与轨道数据中心成熟度相匹配的分阶段采纳模型。框架基于在轨语义缩减原型验证：基于哨兵-2遥感影像（西雅图和班加罗尔）的地球观测流水线，将原始图像转换为紧凑的语义产物，实现99.7%-99.99%的载荷压缩；多通道立体重建原型将约306MB的三维表示压缩至约1.57MB（99.49%压缩率）。结果支持以工作负载为导向的视角——语义抽象能力比单纯算力规模更能决定早期任务的适航性。
原文摘要 · Abstract (English)
Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framework for deciding which tasks belong in orbit versus terrestrial cloud, along with a phased adoption model tied to orbital data center maturity. We ground the framework with in-orbit semantic-reduction prototypes. An Earth-observation pipeline on Sentinel-2 imagery from Seattle and Bengaluru (formerly Bangalore) achieves 99.7-99.99% payload reduction by converting raw imagery to compact semantic artifacts. A multi-pass stereo reconstruction prototype reduces ~306 MB to ~1.57 MB of derived 3D representations (99.49% reduction). These results support a workload-first view in which semantic abstraction, not raw compute scale, drives early workload suitability.
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