Earth+: on-board satellite imagery compression leveraging historical earth observations
March 18, 2024 ยท Declared Dead ยท ๐ International Conference on Architectural Support for Programming Languages and Operating Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kuntai Du, Yihua Cheng, Peder Olsen, Shadi Noghabi, Ranveer Chandra, Junchen Jiang
arXiv ID
2403.11434
Category
cs.NI: Networking & Internet
Cross-listed
cs.DC
Citations
9
Venue
International Conference on Architectural Support for Programming Languages and Operating Systems
Last Checked
3 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Networking & Internet
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
R.I.P.
๐ป
Ghosted
A Survey of Indoor Localization Systems and Technologies
R.I.P.
๐ป
Ghosted
Survey of Important Issues in UAV Communication Networks
R.I.P.
๐ป
Ghosted
Network Function Virtualization: State-of-the-art and Research Challenges
R.I.P.
๐ป
Ghosted
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted