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

arXiv: https://arxiv.org/abs/2410.05665 · HTML: https://mlsanity.com/paper/2410.05665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [In-domain representation learning for remote sensing](https://mlsanity.com/paper/1911.06721/) (2019)
- [LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches](https://mlsanity.com/paper/2207.11896/) (2022)
- [Network Characteristics of LEO Satellite Constellations: A Starlink-Based Measurement from End Users](https://mlsanity.com/paper/2212.13697/) (2022)
- [Energy and Time-Aware Inference Offloading for DNN-based Applications in LEO Satellites](https://mlsanity.com/paper/2311.13509/) (2023)

## Abstract (arXiv)

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.