OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918331649359872 |
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| author | Li, Zhouyu Yang, Zhijin Gu, Huayue Wang, Xiaojian Liu, Yuchen Yu, Ruozhou |
| author_facet | Li, Zhouyu Yang, Zhijin Gu, Huayue Wang, Xiaojian Liu, Yuchen Yu, Ruozhou |
| contents | Earth observation analytics have the potential to transform many sectors. However, due to limited ground connections, it currently takes hours to days to download and analyze Earth observation data, diminishing the value of data for time-sensitive applications like disaster monitoring or search-and-rescue. To enable real-time analytics, we propose OrbitChain, an in-orbit multi-satellite Earth analytics framework. OrbitChain uses a pipelined design to decompose workflows into analytics functions, and orchestrates constellation-wide resources to finish real-time analytics tasks. It provides timely insights to Earth sensing applications and enables advanced workflows like in-orbit tip-and-cue. Hardware-in-the-loop experiments show that OrbitChain can deliver analytics results in minutes, supports up to 60% more analytics workload than existing frameworks, and reduces inter-satellite communication overhead by up to 45%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data Li, Zhouyu Yang, Zhijin Gu, Huayue Wang, Xiaojian Liu, Yuchen Yu, Ruozhou Distributed, Parallel, and Cluster Computing Emerging Technologies Machine Learning Networking and Internet Architecture Earth observation analytics have the potential to transform many sectors. However, due to limited ground connections, it currently takes hours to days to download and analyze Earth observation data, diminishing the value of data for time-sensitive applications like disaster monitoring or search-and-rescue. To enable real-time analytics, we propose OrbitChain, an in-orbit multi-satellite Earth analytics framework. OrbitChain uses a pipelined design to decompose workflows into analytics functions, and orchestrates constellation-wide resources to finish real-time analytics tasks. It provides timely insights to Earth sensing applications and enables advanced workflows like in-orbit tip-and-cue. Hardware-in-the-loop experiments show that OrbitChain can deliver analytics results in minutes, supports up to 60% more analytics workload than existing frameworks, and reduces inter-satellite communication overhead by up to 45%. |
| title | OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.13374 |