一种可扩展的遥感数据人工智能训练平台

Hendrik M. Würz, Kevin Kocon, Barbara Pedretscher, E. Klien, E. Eggeling
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引用次数: 0

摘要

摘要我们提出了一个支持人工智能开发生命周期的平台,重点关注遥感等大数据。我们的目标是由于法律原因或数据合规性而不允许使用现有商业云平台的开发人员。我们平台的灵活实现支持在经典服务器基础设施和内部云上进行部署。我们的目标是可扩展和资源高效执行,独立于特定的人工智能框架和编程语言,以及结果的可重复性,通过基于工作流的计算与数据版本控制工具相结合来实现。通过训练基于人工智能的森林类型分类,证明了该平台的能力。
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A Scalable AI Training Platform for Remote Sensing Data
Abstract. We present a platform to support the AI development lifecycle with focus on large data like remote sensing.We target developers who are not allowed to use existing commercial cloud platforms for legal reasons or data compliance. The flexible implementation of our platform enables a deployment on classic server infrastructures as well as on internal clouds. Our goals of scalable and resource-efficient execution, independence from specific AI frameworks and programming languages, as well as reproducibility of results are met through a workflow-based calculation combined with the tool Data Version Control. The capabilities of the platform are demonstrated by training an AI-based forest type classification.
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