{"ID":2849015,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24242","arxiv_id":"2510.24242","title":"Enabling Near-realtime Remote Sensing via Satellite-Ground Collaboration of Large Vision-Language Models","abstract":"Large vision-language models (LVLMs) have recently demonstrated great potential in remote sensing (RS) tasks (e.g., disaster monitoring) conducted by low Earth orbit (LEO) satellites. However, their deployment in real-world LEO satellite systems remains largely unexplored, hindered by limited onboard computing resources and brief satellite-ground contacts. We propose Grace, a satellite-ground collaborative system designed for near-realtime LVLM inference in RS tasks. Accordingly, we deploy compact LVLM on satellites for realtime inference, but larger ones on ground stations (GSs) to guarantee end-to-end performance. Grace is comprised of two main phases that are asynchronous satellite-GS Retrieval-Augmented Generation (RAG), and a task dispatch algorithm. Firstly, we still the knowledge archive of GS RAG to satellite archive with tailored adaptive update algorithm during limited satellite-ground data exchange period. Secondly, propose a confidence-based test algorithm that either processes the task onboard the satellite or offloads it to the GS. Extensive experiments based on real-world satellite orbital data show that Grace reduces the average latency by 76-95% compared to state-of-the-art methods, without compromising inference accuracy.","short_abstract":"Large vision-language models (LVLMs) have recently demonstrated great potential in remote sensing (RS) tasks (e.g., disaster monitoring) conducted by low Earth orbit (LEO) satellites. However, their deployment in real-world LEO satellite systems remains largely unexplored, hindered by limited onboard computing resource...","url_abs":"https://arxiv.org/abs/2510.24242","url_pdf":"https://arxiv.org/pdf/2510.24242v1","authors":"[\"Zihan Li\",\"Jiahao Yang\",\"Yuxin Zhang\",\"Zhe Chen\",\"Yue Gao\"]","published":"2025-10-28T09:48:26Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\",\"cs.LG\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}