ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiki...
| Authors | Bohan Zhang, Chenyu Xu, Yijie Mao et al. |
|---|---|
| Primary category | cs.NE |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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