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在轨演示可重新训练的机器学习有效载荷,用于处理光学图像
Gonzalo Mateo-Garcia1,2, Josh Veitch-Michaelis3,4, Cormac Purcell3,5
1Trillium Technologies Ltd., 27-29 South Lambeth Road, London, SW8 1SZ, UK. gonzalo@trillium.tech.
Scientific reports
|June 27, 2023
概括
这项研究表明,人工智能驱动的机器学习 (ML) 负载WorldFloods成功地从太空绘制了洪水. 该系统能够实现机载处理和远程模型更新,以实现地球观测的持续学习.
科学领域:
- 太空科学 太空科学
- 人工智能的人工智能
- 地球观测 地球观测
背景情况:
- 太空的认知云计算 (3CS) 是一个新兴的领域,推动着行星观测和深空探索方面的创新.
- 机器学习 (ML) 有效载荷对于从太空中的内置传感器中提取高级信息至关重要.
研究的目的:
- 为了证明一个运行ML有效载荷",WorldFloods",在轨道卫星上用于洪水检测和绘制地图.
- 评估机载部署的ML模型性能,并评估远程模型再培训和更新的可行性.
主要方法:
- 在关键的内置处理约束下对各种细分模型进行比较.
- 从卫星图像直接生成矢量化洪水多边形.
- 在需要时使用下链传感器数据对ML模型进行重新训练.
- 将更新的模型与卫星连接起来,以便实时处理新收购.
主要成果:
- 通过使用WorldFloods ML有效载荷成功生成压缩洪水地图.
- 从完整的"卫星卫星2"中制作矢量化洪水多边形的演示.
- 在上链接后对重新训练模型在新的卫星图像上的性能进行验证.
- 在轨道上更新ML模型的概念验证.
结论:
- 基于机器学习的模型可以在轨道上有效地更新,从而实现机上和地面处理的敏捷集成.
- 这项研究为持续学习和适应性ML应用在基于太空的地球观测中铺平了道路.
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