Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction
arXiv cs.CV / 3/24/2026
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Key Points
- The paper introduces a workload-first framework for deciding whether specific tasks should run in space (in orbit) or remain on terrestrial cloud infrastructure, using semantic abstraction as the key decision lens.
- It proposes a phased adoption approach linked to the maturity of orbital data centers, arguing that early feasibility depends more on reducing and structuring data than on scaling raw compute.
- In-orbit semantic-reduction prototypes are used to ground the framework, including an Earth-observation pipeline that converts Sentinel-2 raw imagery into compact semantic artifacts with 99.7–99.99% payload reduction.
- A multi-pass stereo reconstruction prototype further demonstrates data efficiency by shrinking about 306 MB to ~1.57 MB of derived 3D representations (99.49% reduction).
- The authors conclude that semantic abstraction—not sheer compute scale—makes data-intensive AI workloads more suitable for early deployment in orbital environments.
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