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.

翻译：地球观测系统通过卫星网络收集和分析关键全球数据，在实现可持续发展目标方面发挥着至关重要的作用。这些系统对于制图、灾害监测和资源管理等任务至关重要，但在处理和传输大量地球观测数据方面面临挑战，尤其是在农业和实时灾害响应等专业领域。领域自适应的大型语言模型通过促进海量地球观测数据与语义地球观测数据之间的数据融合，提供了一种有前景的解决方案。通过改进不同数据集的集成与解释，大型语言模型解决了在农业和灾害响应应用中处理专业信息的挑战。这种融合提高了传输数据的准确性和相关性。本文提出了一种用于地球观测卫星网络的语义通信框架，旨在通过认知处理技术提高数据传输效率和整体系统性能。所提出的系统采用离散任务导向的源信道编码和语义数据增强技术，以聚焦相关信息，同时最小化通信开销。通过集成认知语义处理和星间链路，该框架增强了对多光谱卫星图像的分析与传输，改善了目标检测、模式识别和实时决策能力。认知语义增强技术的引入使卫星能够处理和传输语义信息，从而提升了对变化环境和应用需求的适应性。这种端到端架构专为支持6G等下一代卫星网络而设计，并在效率和准确性方面展现出显著改进。