让卫星和地面协同运行大模型，实时分析地球观测图像。
A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation
- 卫星跑轻量模型，地面处理复杂任务，分工协作。
- 通过渐进置信度与多尺度预处理，减少传输数据量51.2%。
- 适用于灾害、极端天气等需要快速响应的遥感应用。
近期，大型视觉语言模型（LVLM）在数据中心展现出强大的低地球轨道（LEO）卫星地球观测图像分析能力。然而，卫星高速运动、卫星-地面站（GS）通信窗口短暂以及图像数据量庞大，带来了严峻的数据下载挑战。为支持近实时地球观测应用（如灾害与极端天气监测），我们探索了在LEO卫星网络中部署LVLM的方案，并设计了SpaceVerse——一种高效的卫星-地面协同LVLM推理系统。首先，我们在卫星上部署轻量级LVLM处理简单任务，而常规LVLM在地面站执行计算密集型任务。其次，提出一种计算与通信协同设计框架，包含渐进置信度网络和基于注意力的多尺度预处理，分别用于识别可在卫星端推理的数据，以及在卫星-地面传输前减少数据冗余。我们在真实LEO卫星星座和数据集上实现并评估了SpaceVerse，相比最先进基线，平均准确率提升31.2%，延迟降低51.2%。
原文摘要 · Abstract (English)
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.
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