# On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks arXiv: https://arxiv.org/abs/2409.15246 ยท HTML: https://mlsanity.com/paper/2409.15246/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 97 references that are in the map. - First posted on arXiv 23 Sep 2024. ## Builds on (most cited first, 30 of 37) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020) - [OpenAGI: When LLM Meets Domain Experts](https://mlsanity.com/paper/2304.04370/) (2023) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020) - [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018) - [Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks](https://mlsanity.com/paper/2309.16499/) (2023) - [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020) - [SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model](https://mlsanity.com/paper/2305.02034/) (2023) - [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024) - [Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation](https://mlsanity.com/paper/2209.10382/) (2022) - [Large Language Models for Networking: Applications, Enabling Techniques, and Challenges](https://mlsanity.com/paper/2311.17474/) (2023) - [Learning Semantics: An Opportunity for Effective 6G Communications](https://mlsanity.com/paper/2110.08049/) (2021) - [D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications](https://mlsanity.com/paper/2403.07338/) (2024) - [Fine-grained Recognition with Learnable Semantic Data Augmentation](https://mlsanity.com/paper/2309.00399/) (2023) - [Semantic Communications Based on Adaptive Generative Models and Information Bottleneck](https://mlsanity.com/paper/2309.02387/) (2023) - [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024) - [Deep Joint Source-Channel and Encryption Coding: Secure Semantic Communications](https://mlsanity.com/paper/2208.09245/) (2022) - [Semantic Importance-Aware Communications Using Pre-trained Language Models](https://mlsanity.com/paper/2302.07142/) (2023) - [A Survey on Non-Geostationary Satellite Systems: The Communication Perspective](https://mlsanity.com/paper/2107.05312/) (2021) - [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019) - [Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models](https://mlsanity.com/paper/2403.02756/) (2024) - [From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication](https://mlsanity.com/paper/2406.05437/) (2024) - [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022) - [On the Role of ViT and CNN in Semantic Communications: Analysis and Prototype Validation](https://mlsanity.com/paper/2306.02759/) (2023) - [DALLMi: Domain Adaption for LLM-based Multi-label Classifier](https://mlsanity.com/paper/2405.01883/) (2024) - [Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View](https://mlsanity.com/paper/1609.08583/) (2016) - [Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation](https://mlsanity.com/paper/1707.07321/) (2017) - [Task-oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications](https://mlsanity.com/paper/2102.07166/) (2021) ## Built on by (most cited first, 4 of 4) - [Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification](https://mlsanity.com/paper/2411.00209/) (2024) - [A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives](https://mlsanity.com/paper/2503.09903/) (2025) - [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025) - [Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery](https://mlsanity.com/paper/2602.00136/) (2026) ## Abstract (arXiv) 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.