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

2025-03-12

AI Summary

Ensuring semantic integrity of remote sensing data under bandwidth-constrained transmission remains challenging. Method: This paper proposes a task-oriented, dual-dimensional semantic distortion quantification paradigm—jointly modeling source coding distortion and channel transmission distortion—and constructs a generalizable semantic loss prediction model. Leveraging real Earth observation data and four lightweight/knowledge-distilled models (EfficientViT, MobileViT, ResNet50-DINO, ResNet8-KD), the approach integrates semantic communication, remote sensing image compression, channel modeling, and task-driven evaluation to inversely model downstream task accuracy degradation as a function of semantic distortion. Results: The model accurately predicts task performance decline across diverse compression ratios and channel conditions, achieving a mean absolute error of <3.2%. It significantly enhances semantic reliability and practical utility of remote sensing data transmission in bandwidth-limited scenarios.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Data Compression

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Addresses limited satellite bandwidth in Earth Observation systems.
• Models semantic loss in data transmission and processing.
• Evaluates task-oriented ML models for lossy image datasets.

Innovation

• Semantic Communication prioritizes essential data transmission
• Framework models semantic loss using real-world EO datasets
• Task-oriented ML models evaluate semantic loss accuracy