用语义推理和深度学习提升卫星遥感数据传输效率
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks
- 将大语言模型用于遥感数据的语义理解与融合
- 通过在轨处理提升数据传输效率，支持实时决策
- 适合6G卫星通信与灾害应急等高时效场景
地球观测（EO）系统在制图、灾害监测和资源管理中至关重要，但面临海量数据处理与传输的挑战，尤其在精准农业和实时灾情响应等专业领域。搭载遥感技术的地球观测卫星通过机载传感器和物联网地面设备采集数据，提供远程信息。领域自适应的大语言模型（LLMs）可整合原始与处理后的EO数据，通过领域适配提升多源数据的融合与分析能力，应对农业与灾情响应中复杂数据集的挑战。该数据融合由LLM引导，显著提高信息传递的精度与相关性。本研究全面探讨了语义推理与深度学习在先进地球观测系统中的应用，提出一种面向遥感卫星网络的新型语义通信架构，旨在通过语义处理方法提升数据传输效率。近期在轨处理技术的进步使轨道数据管理具备可靠性、灵活性与低功耗特性，结合抗辐射与可重构技术，在恶劣太空环境中保障稳定性能。这些进展共同推动下一代卫星任务实现更强的处理能力，对6G卫星通信中的操作灵活性与实时决策具有重要意义。
原文摘要 · Abstract (English)
Earth Observation (EO) systems are crucial for cartography, disaster surveillance, and resource administration. Nonetheless, they encounter considerable obstacles in the processing and transmission of extensive data, especially in specialized domains such as precision agriculture and real-time disaster response. Earth observation satellites, outfitted with remote sensing technology, gather data from onboard sensors and IoT-enabled terrestrial objects, delivering important information remotely. Domain-adapted Large Language Models (LLMs) provide a solution by enabling the integration of raw and processed EO data. Through domain adaptation, LLMs improve the assimilation and analysis of many data sources, tackling the intricacies of specialized datasets in agriculture and disaster response. This data synthesis, directed by LLMs, enhances the precision and pertinence of conveyed information. This study provides a thorough examination of using semantic inference and deep learning for sophisticated EO systems. It presents an innovative architecture for semantic communication in EO satellite networks, designed to improve data transmission efficiency using semantic processing methodologies. Recent advancements in onboard processing technologies enable dependable, adaptable, and energy-efficient data management in orbit. These improvements guarantee reliable performance in adverse space circumstances using radiation-hardened and reconfigurable technology. Collectively, these advancements enable next-generation satellite missions with improved processing capabilities, crucial for operational flexibility and real-time decision-making in 6G satellite communication.
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