边云协同处理卫星图像，高效识别人造建筑。
Edge-Cloud Collaborative Satellite Image Analysis for Efficient Man-Made Structure Recognition
- 边缘端用轻量模型初筛，云端用复杂模型精分类。
- 相比传统方法，延迟降低且准确率更高。
- 适合需要实时与高精度的遥感应用开发者。
高分辨率卫星影像的日益普及为众多应用带来机遇，但及时准确地处理海量数据仍面临挑战。本文提出一种融合边缘与云计算的卫星图像分析架构，用于更有效地识别自然景观中的人造结构。通过在边缘部署轻量模型，系统首先从卫星影像中初步识别潜在的人造结构；这些候选图像随后传输至云端，由更复杂的模型进行精细化分类，确定具体结构类型。研究重点关注延迟与准确率之间的权衡，因为高效模型常以牺牲准确率为代价。我们在虚拟环境中对比了该混合边云方法与传统“弯管”模式，并引入一种实际可用的轻量级模型，与现有边缘部署模型在准确率和延迟方面进行比较。结果表明，边云协同模型不仅因减少数据传输而显著降低整体延迟，还能保持高准确率，在该场景下相较传统方法有显著提升。
原文摘要 · Abstract (English)
The increasing availability of high-resolution satellite imagery has created immense opportunities for various applications. However, processing and analyzing such vast amounts of data in a timely and accurate manner poses significant challenges. The paper presents a new satellite image processing architecture combining edge and cloud computing to better identify man-made structures against natural landscapes. By employing lightweight models at the edge, the system initially identifies potential man-made structures from satellite imagery. These identified images are then transmitted to the cloud, where a more complex model refines the classification, determining specific types of structures. The primary focus is on the trade-off between latency and accuracy, as efficient models often sacrifice accuracy. We compare this hybrid edge-cloud approach against traditional "bent-pipe" method in virtual environment experiments as well as introduce a practical model and compare its performance with existing lightweight models for edge deployment, focusing on accuracy and latency. The results demonstrate that the edge-cloud collaborative model not only reduces overall latency due to minimized data transmission but also maintains high accuracy, offering substantial improvements over traditional approaches under this scenario.
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