Problem
Research questions and friction points this paper is trying to address.
semantic delivery
Earth observation satellite networks
resource-constrained communication
mission-oriented processing
on-board processing
SpaceRipple: Lightweight Semantic Delivery for Mission-Oriented LEO Earth Observation Satellite Networks
🤖 AI Summary
This work addresses the tension between the massive data volume of high-resolution Earth observation imagery and the limited bandwidth of space-to-ground links in low Earth orbit satellite networks, where users prioritize task-relevant semantics over raw pixels. To this end, the authors propose SpaceRipple, a lightweight framework that pioneers semantic communication in satellite networks by establishing an end-to-end collaborative semantic delivery pipeline: sensing satellites adaptively compress imagery and generate metadata, while edge-computing satellites reconstruct representations and perform task-oriented semantic inference. The approach innovatively integrates compressed sensing with a Mixture-of-Experts (MoE)-enhanced module to improve robustness under degraded input conditions. Experiments demonstrate that SpaceRipple significantly enhances semantic detection accuracy while preserving reconstruction fidelity and substantially reducing bandwidth consumption, thereby validating the feasibility of efficient and reliable Earth observation under resource-constrained scenarios.
📝 Abstract
Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limited. In many time-sensitive missions, ground users require mission-relevant semantic information rather than a full raw-image downlink. This paper proposes SpaceRipple, a lightweight framework for mission-oriented semantic delivery and on-board processing in Earth observation satellite networks. A sensing satellite performs adaptive compression and metadata generation to reduce inter-satellite traffic, while an edge computing satellite restores the received representation and extracts task-relevant semantic information. Unlike fidelity-driven image transmission, SpaceRipple coordinates compression, forwarding, restoration, and semantic inference within a collaborative pipeline, enabling semantic-oriented delivery instead of pixel-level image delivery. A compression-aware MoE enhancement module is further introduced to improve robustness under degraded visual inputs. Experimental results show that SpaceRipple achieves favorable reconstruction quality, improved semantic detection performance, and substantial bandwidth savings, demonstrating its potential for efficient and reliable Earth observation under constrained satellite-network resources.
Innovation
Methods, ideas, or system contributions that make the work stand out.
semantic delivery
lightweight framework
on-board processing
compression-aware MoE
LEO satellite networks
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