# Semantic and goal-oriented edge computing for satellite Earth Observation

arXiv: https://arxiv.org/abs/2408.15639 · HTML: https://mlsanity.com/paper/2408.15639/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem](https://mlsanity.com/paper/2503.04803/) (2025)
- [Quantization-Aware Collaborative Inference for Large Embodied AI Models](https://mlsanity.com/paper/2602.13052/) (2026)
- [Impact of Atmospheric Turbulence and Pointing Error on Earth Observation](https://mlsanity.com/paper/2605.22268/) (2026)

## Abstract (arXiv)

The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources for data-intensive applications. We use Earth Observation (EO) as a canonical functionality of satellites and review its main characteristics and challenges. We identify the potential of the space segment, represented by a low Earth orbit (LEO) satellite constellation, to serve as an edge layer for distributed intelligence. Based on that, propose a system architecture that supports semantic and goal-oriented applications for image reconstruction and object detection and localization. The simulation results show the intricate trade-offs among energy, time, and task-performance using a real dataset and State-of-the-Art (SoA) processing and communication parameters.