Publikationen

Autor	Würz, Hendrik Martin; Kocon, Kevin; Pedretscher, Barbara; Klien, Eva; Eggeling, Eva
Datum	2023
Art	Conference Paper
Abstrakt	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.
Konferenz	Conference on Geographic Information Science 2023
Publisher	Copernicus Publications
Projekt	Die Digitalisierung des Waldes
Url	https://publica.fraunhofer.de/handle/publica/444955

A Scalable AI Training Platform for Remote Sensing Data