Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Buchkapitel |
| DOI | 10.1109/IGARSS53475.2024.10640742 |
| Titel (primär) | On-demand Earth System Data Cubes |
| Titel (sekundär) | 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 07-12 July 2024 |
| Autor | Montero, D.; Aybar, C.; Kraemer, G.; Söchting, M.; Teber, K.; Mahecha, M.D. |
| Quelle | International Geoscience and Remote Sensing Symposium |
| Erscheinungsjahr | 2024 |
| Department | RS |
| Band/Volume | IGARSS 2024 |
| Seite von | 7529 |
| Seite bis | 7532 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Abstract | Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30293 |
| Montero, D., Aybar, C., Kraemer, G., Söchting, M., Teber, K., Mahecha, M.D. (2024): On-demand Earth System Data Cubes 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 07-12 July 2024 International Geoscience and Remote Sensing Symposium IGARSS 2024 Institute of Electrical and Electronics Engineers (IEEE), New York, NY, p. 7529 - 7532 10.1109/IGARSS53475.2024.10640742 |
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