Würz, Hendrik M. ; Kocon, Kevin ; Pedretscher, Barbara ; Klien, Eva ; Eggeling, Eva (2023)
A Scalable AI Training Platform for Remote Sensing Data.
In: AGILE: GIScience Series, 4
doi: 10.5194/agile-giss-4-53-2023
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Würz, Hendrik M. ; Kocon, Kevin ; Pedretscher, Barbara ; Klien, Eva ; Eggeling, Eva |
| Art des Eintrags: | Bibliographie |
| Titel: | A Scalable AI Training Platform for Remote Sensing Data |
| Sprache: | Englisch |
| Publikationsjahr: | 6 Juni 2023 |
| Ort: | k.A. |
| Verlag: | Copernicus Publications |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | AGILE: GIScience Series |
| Jahrgang/Volume einer Zeitschrift: | 4 |
| DOI: | 10.5194/agile-giss-4-53-2023 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Artificial intelligence (AI), Workflow management, Cloud computing, Remote sensing |
| Zusätzliche Informationen: | Issue to 26th AGILE Conference on Geographic Information Science “Spatial data for design”, Delft, the Netherlands, 13.-16.06.2023 ; Art.No.: 53 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 19 Jul 2023 07:30 |
| Letzte Änderung: | 19 Jul 2023 12:22 |
| PPN: | 509800416 |
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| Suche nach Titel in: | TUfind oder in Google |
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