Earth+: on-board satellite imagery compression leveraging historical earth observations
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Abstract (arXiv)
With the increasing deployment of earth observation satellite constellations, the downlink (satellite-to-ground) capacity often limits the freshness, quality, and coverage of the imagery data available to applications on the ground. To overcome the downlink limitation, we present Earth+, a new satellite imagery compression system that, instead of compressing each image individually, pinpoints and downloads only recent imagery changes with respect to the history reference images. To minimize the amount of changes, it is critical to make reference images as fresh as possible. Earth+ enables each satellite to choose fresh reference images from not only its own history images but also past images of other satellites from an entire satellite constellation. To share reference images across satellites, Earth+ utilizes the limited capacity of the existing uplink (ground-to-satellite) by judiciously selecting and compressing reference images while still allowing accurate change detection. In short, Earth+ is the first to make reference-based compression efficient, by enabling constellation-wide sharing of fresh reference images across satellites. Our evaluation shows that Earth+ can reduce the downlink usage by a factor of 3.3 compared to state-of-the-art on-board image compression techniques while not sacrificing image quality, or using more on-board computing or storage resources, or more uplink bandwidth than currently available.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2024.
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Citing papers per year: 2025: 3; 2026: 3.
6 papers.
- 2025Lossy Neural Compression for Geospatial Analytics: A Reviewcited by 10
- 2025NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observationcited by 7
- 2025OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Datacited by 0
- 2026Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reductioncited by 0
- 2026Exploiting In-Sensor Computing for Energy-Efficient Earth Observationcited by 0
- 2026Clear-Weighted Bit Allocation for Satellite Downlinkscited by 0
- 2026Clear-Weighted Bit Allocation for Satellite Downlinks2 Aug 2026
- 2026Exploiting In-Sensor Computing for Energy-Efficient Earth Observation31 May 2026
- 2026Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction9 May 2026
- 2025NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation19 Oct 2025
- 2025OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data18 Aug 2025
- 2025Lossy Neural Compression for Geospatial Analytics: A Review3 Mar 2025