# A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives

arXiv: https://arxiv.org/abs/2503.09903 · HTML: https://mlsanity.com/paper/2503.09903/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [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)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks](https://mlsanity.com/paper/2310.19909/) (2023)
- [Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G](https://mlsanity.com/paper/2303.01633/) (2023)
- [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)
- [Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification](https://mlsanity.com/paper/2411.00209/) (2024)
- [GEO Payload Power Minimization: Joint Precoding and Beam Hopping Design](https://mlsanity.com/paper/2208.10474/) (2022)
- [Cognitive Semantic Augmentation LEO Satellite Networks for Earth Observation](https://mlsanity.com/paper/2410.21916/) (2024)

## Built on by (most cited first, 2 of 2)

- [Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery](https://mlsanity.com/paper/2602.00136/) (2026)
- [LIMODENet: Attention-Free Compact Encoders for Information-Preserving Onboard Satellite Image Restoration](https://mlsanity.com/paper/2609.14690/) (2026)

## Abstract (arXiv)

The integration of machine learning (ML) has significantly enhanced the capabilities of Earth Observation (EO) systems by enabling the extraction of actionable insights from complex datasets. However, the performance of data-driven EO applications is heavily influenced by the data collection and transmission processes, where limited satellite bandwidth and latency constraints can hinder the full transmission of original data to the receivers. To address this issue, adopting the concepts of Semantic Communication (SC) offers a promising solution by prioritizing the transmission of essential data semantics over raw information. Implementing SC for EO systems requires a thorough understanding of the impact of data processing and communication channel conditions on semantic loss at the processing center. This work proposes a novel data-fitting framework to empirically model the semantic loss using real-world EO datasets and domain-specific insights. The framework quantifies two primary types of semantic loss: (1) source coding loss, assessed via a data quality indicator measuring the impact of processing on raw source data, and (2) transmission loss, evaluated by comparing practical transmission performance against the Shannon limit. Semantic losses are estimated by evaluating the accuracy of EO applications using four task-oriented ML models, EfficientViT, MobileViT, ResNet50-DINO, and ResNet8-KD, on lossy image datasets under varying channel conditions and compression ratios. These results underpin a framework for efficient semantic-loss modeling in bandwidth-constrained EO scenarios, enabling more reliable and effective operations.