# Satellite edge computing for real-time and very-high resolution Earth observation

arXiv: https://arxiv.org/abs/2212.12912 · HTML: https://mlsanity.com/paper/2212.12912/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2022.

## Built on by (most cited first, 12 of 12)

- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives](https://mlsanity.com/paper/2503.09903/) (2025)
- [Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning](https://mlsanity.com/paper/2501.06244/) (2025)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)
- [Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks](https://mlsanity.com/paper/2409.07911/) (2024)
- [OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data](https://mlsanity.com/paper/2508.13374/) (2025)
- [Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery](https://mlsanity.com/paper/2602.00136/) (2026)
- [Structured Bitmap-to-Mesh Triangulation for Geometry-Aware Discretization of Image-Derived Domains](https://mlsanity.com/paper/2602.19474/) (2026)
- [iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery](https://mlsanity.com/paper/2603.18539/) (2026)
- [Impact of Atmospheric Turbulence and Pointing Error on Earth Observation](https://mlsanity.com/paper/2605.22268/) (2026)
- [Exploiting In-Sensor Computing for Energy-Efficient Earth Observation](https://mlsanity.com/paper/2606.01271/) (2026)
- [SAT-Edge-Agent: Hardware-in-the-Loop Edge-Agent Orchestration for Onboard Satellite Intelligence](https://mlsanity.com/paper/2608.03728/) (2026)

## Abstract (arXiv)

In real-time and high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture images that are subsequently transmitted to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology or environmental monitoring, but can also be employed in near-real time operation for disaster detection, identification, and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among the satellites for processing. In this paper, we address an energy minimization problem based on a general satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation. Our results illustrate that the optimal allocation of data and selection of the compression parameters increase the amount of images that the system can support by a factor of 12 when compared to directly downloading the data. Further, energy savings greater than 11% were observed in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that potential energy savings can be as high as 92%.