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

Hong-fu Chou, Vu Nguyen Ha, Prabhu Thiruvasagam, Thanh-Dung Le, Geoffrey Eappen, Ti Ti Nguyen, Luis M. Garces-Socarras, Jorge L. Gonzalez-Rios, Juan Carlos Merlano-Duncan, Symeon Chatzinotas

TL;DR

This study provides a thorough examination of using semantic inference and deep learning for sophisticated EO systems and presents an innovative architecture for semantic communication in EO satellite networks, designed to improve data transmission efficiency using semantic processing methodologies.

Abstract

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.

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

TL;DR

This study provides a thorough examination of using semantic inference and deep learning for sophisticated EO systems and presents an innovative architecture for semantic communication in EO satellite networks, designed to improve data transmission efficiency using semantic processing methodologies.

Abstract

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.
Paper Structure (23 sections, 7 figures, 1 table)

This paper contains 23 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Semantic data processing for EO applications
  3. Computational semantic compression
  4. Fusion of semantic trajectories
  5. Advancements in Semantic Comprehension
  6. Establishment of networks for semantic communication
  7. Semantic networks evolving toward SemCom networks
  8. Semantic communication perspective and characteristics
  9. Semantic joint source-channel coding
  10. Enhancing Domain-Specific Semantic Effectiveness with Large Language Models
  11. Domain-adapted large language model
  12. Design of semantic effective plane
  13. Automating earth observation with LLMs integration
  14. On-board deep learning and hardware design for resource-efficient EO systems
  15. Revisit on the effectiveness of on-board EO deep learning
  16. ...and 8 more sections

Figures (7)

  • Figure 1: The system model of semantic-oriented EO satellite networks.
  • Figure 2: The block diagram for the proposed semantic inference-based earth observation adapting the techniques in chen2024semantic.
  • Figure 3: The proposed semantic effectiveness plane applying semantic processing with RAG LLM technique
  • Figure 4: The proposed EO on-board processing for semantic communication inspired by Perez2020.
  • Figure 5: Top1 accuracy using DT-JSCC based on 16PSK/16APSK AWGN and Rician channel.
  • ...and 2 more figures