标题： 用于地球观测的卫星-地面协同大型视觉-语言模型系统 显示英文标题

标题： A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation

摘要： 最近，大型视觉-语言模型（LVLMs）在数据中心中释放出强大的分析能力，用于低地球轨道（LEO）卫星地球观测图像。 然而，快速的卫星运动、短暂的卫星-地面站（GS）接触窗口以及图像的大尺寸带来了数据下载的挑战。 为了实现接近实时的地球观测应用（例如，灾害和极端天气监测），我们应该探索如何在LEO卫星网络中部署LVLM，并设计SpaceVerse，一个高效的卫星-地面协同LVLM推理系统。 为此，首先，我们在卫星上部署紧凑的LVLMs以处理轻量级任务，而常规的LVLMs则在GS上运行以处理计算密集型任务。 然后，我们提出了一种计算与通信协同设计框架，包括一个渐进置信度网络和基于注意力的多尺度预处理，分别用于识别卫星上的推理数据，并在卫星-GS传输前减少数据冗余。 我们在真实的LEO卫星星座和数据集上实现了并评估了SpaceVerse，与最先进的基线相比，准确率平均提高了31.2%，延迟减少了51.2%。 显示英文摘要

摘要： Recently, large vision-language models (LVLMs) unleash powerful analysis capabilities for low Earth orbit (LEO) satellite Earth observation images in the data center. However, fast satellite motion, brief satellite-ground station (GS) contact windows, and large size of the images pose a data download challenge. To enable near real-time Earth observation applications (e.g., disaster and extreme weather monitoring), we should explore how to deploy LVLM in LEO satellite networks, and design SpaceVerse, an efficient satellite-ground synergistic LVLM inference system. To this end, firstly, we deploy compact LVLMs on satellites for lightweight tasks, whereas regular LVLMs operate on GSs to handle computationally intensive tasks. Then, we propose a computing and communication co-design framework comprised of a progressive confidence network and an attention-based multi-scale preprocessing, used to identify on-satellite inferring data, and reduce data redundancy before satellite-GS transmission, separately. We implement and evaluate SpaceVerse on real-world LEO satellite constellations and datasets, achieving a 31.2% average gain in accuracy and a 51.2% reduction in latency compared to state-of-the-art baselines.