# Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery

arXiv: https://arxiv.org/abs/2602.00136 · HTML: https://mlsanity.com/paper/2602.00136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention](https://mlsanity.com/paper/2305.07027/) (2023)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications](https://mlsanity.com/paper/2403.07338/) (2024)
- [Satellite edge computing for real-time and very-high resolution Earth observation](https://mlsanity.com/paper/2212.12912/) (2022)
- [Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models](https://mlsanity.com/paper/2409.03901/) (2024)
- [Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey](https://mlsanity.com/paper/2304.13008/) (2023)
- [Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems](https://mlsanity.com/paper/1903.08566/) (2019)
- [Edge AI Empowered Physical Layer Security for 6G NTN: Potential Threats and Future Opportunities](https://mlsanity.com/paper/2401.01005/) (2023)
- [On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks](https://mlsanity.com/paper/2409.15246/) (2024)
- [A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives](https://mlsanity.com/paper/2503.09903/) (2025)

## Abstract (arXiv)

Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-use analysis. Although these applications benefit from detailed visual data, the resulting data volumes impose significant challenges on satellite communication systems constrained by limited bandwidth, power, and dynamic link conditions. To address these limitations, this paper investigates Deep Joint Source-Channel Coding (DJSCC) as an effective source-channel paradigm for the transmission of EO imagery. We focus on two complementary aspects of semantic loss in DJSCC-based systems. First, a reconstruction-centric framework is evaluated by analyzing the semantic degradation of reconstructed images under varying compression ratios and channel signal-to-noise ratios (SNR). Second, a task-oriented framework is developed by integrating DJSCC with lightweight, application-specific models (e.g., EfficientViT), with performance measured using downstream task accuracy rather than pixel-level fidelity. Based on extensive empirical analysis, we propose a unified semantic loss framework that captures both reconstruction-centric and task-oriented performance within a single model. This framework characterizes the implicit relationship between JSCC compression, channel SNR, and semantic quality, offering actionable insights for the design of robust and efficient EO imagery transmission under resource-constrained satellite links.