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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.05665 |
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| _version_ | 1866913536034209792 |
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| author | Sheng, Kaicheng Xue, Junxiao Zhang, Hui |
| author_facet | Sheng, Kaicheng Xue, Junxiao Zhang, Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Edge-Cloud Collaborative Satellite Image Analysis for Efficient Man-Made Structure Recognition Sheng, Kaicheng Xue, Junxiao Zhang, Hui Computer Vision and Pattern Recognition 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. |
| title | Edge-Cloud Collaborative Satellite Image Analysis for Efficient Man-Made Structure Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.05665 |