Edge-Cloud Collaborative Satellite Image Analysis for Efficient Man-Made Structure Recognition

TL;DR

This paper introduces a hybrid edge-cloud satellite image analysis system that efficiently detects man-made structures with reduced latency and high accuracy, outperforming traditional methods.

Contribution

It proposes a novel edge-cloud architecture combining lightweight edge models with cloud refinement for improved satellite image analysis.

Findings

Reduces data transmission and latency compared to traditional methods.

Maintains high accuracy in identifying man-made structures.

Outperforms existing lightweight models in experiments.

Abstract

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.