On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks

Earth Observation (EO) systems play a crucial role in achieving Sustainable
Development Goals by collecting and analyzing vital global data through
satellite networks. These systems are essential for tasks like mapping,
disaster monitoring, and resource management, but they face challenges in
processing and transmitting large volumes of EO data, especially in specialized
fields such as agriculture and real-time disaster response. Domain-adapted
Large Language Models (LLMs) provide a promising solution by facilitating data
fusion between extensive EO data and semantic EO data. By improving integration
and interpretation of diverse datasets, LLMs address the challenges of
processing specialized information in agriculture and disaster response
applications. This fusion enhances the accuracy and relevance of transmitted
data. This paper presents a framework for semantic communication in EO
satellite networks, aimed at improving data transmission efficiency and overall
system performance through cognitive processing techniques. The proposed system
employs Discrete-Task-Oriented Source-Channel Coding (DT-JSCC) and Semantic
Data Augmentation (SA) to focus on relevant information while minimizing
communication overhead. By integrating cognitive semantic processing and
inter-satellite links, the framework enhances the analysis and transmission of
multispectral satellite imagery, improving object detection, pattern
recognition, and real-time decision-making. The introduction of Cognitive
Semantic Augmentation (CSA) allows satellites to process and transmit semantic
information, boosting adaptability to changing environments and application
needs. This end-to-end architecture is tailored for next-generation satellite
networks, such as those supporting 6G, and demonstrates significant
improvements in efficiency and accuracy.