Rizzoli, Paola und Carcereri, Daniel und Becker Campos, Alexandre und Gollin, Nicola und Dell'Amore, Luca und Ghio, Federico und Bueso Bello, Jose Luis und Gonzalez, Carolina und Martone, Michele und Zink, Manfred (2026) AI4TDX: How can AI leverage the exploitation of TanDEM-X data to jointly benefit science and shape future mission concepts? In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. European Conference on Synthetic Aperture Radar (EUSAR), 2026-06-08 - 2026-06-11, Baden-Baden, Germany. ISSN 2197-4403.
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Kurzfassung
Over the past 15 years, TanDEM-X has demonstrated its groundbreaking potential, setting a new standard in digital elevation model (DEM) products based on single-pass synthetic aperture radar interferometry (InSAR) and establishing itself as a milestone for Earth Observation (EO). Beyond its success in DEM generation, the mission has also proven to be of enormous value for scientific applications, enabling novel insights across a wide range of disciplines. When combined with artificial intelligence (AI), TanDEM-X offers even greater opportunities, both to support the scientific community through enhanced data analysis and interpretation, and to drive the development of future mission concepts. The synergy between AI and bistatic InSAR configurations holds immense potential, especially when leveraged in a physics-informed manner rather than considering AI as a mere black box. This paper presents a summary of ongoing activities at the Radar Science Group of the DLR Microwaves and Radar Institute that integrate AI with TanDEM-X, aiming to improve performance and extract valuable insights at all stages of the SAR processing chain, from raw data handling to high-level scientific applications. The results highlight how the intelligent fusion of AI and bistatic radar techniques can unlock new levels of efficiency, accuracy, and scientific understanding, paving the way for the design and optimization of future SAR mission concepts.
| elib-URL des Eintrags: | https://elib.dlr.de/223577/ |
|---|---|
| Dokumentart: | Konferenzbeitrag (Vortrag) |
| Titel: | AI4TDX: How can AI leverage the exploitation of TanDEM-X data to jointly benefit science and shape future mission concepts? |
| Autoren: | |
| Datum: | 2026 |
| Erschienen in: | Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR |
| Referierte Publikation: | Ja |
| Open Access: | Nein |
| Gold Open Access: | Nein |
| In SCOPUS: | Ja |
| In ISI Web of Science: | Nein |
| ISSN: | 2197-4403 |
| Status: | akzeptierter Beitrag |
| Stichwörter: | AI4SAR, TanDEM-X, Deep Learning, AI |
| Veranstaltungstitel: | European Conference on Synthetic Aperture Radar (EUSAR) |
| Veranstaltungsort: | Baden-Baden, Germany |
| Veranstaltungsart: | internationale Konferenz |
| Veranstaltungsbeginn: | 8 Juni 2026 |
| Veranstaltungsende: | 11 Juni 2026 |
| Veranstalter : | VDE |
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr |
| HGF - Programm: | Raumfahrt |
| HGF - Programmthema: | Erdbeobachtung |
| DLR - Schwerpunkt: | Raumfahrt |
| DLR - Forschungsgebiet: | R EO - Erdbeobachtung |
| DLR - Teilgebiet (Projekt, Vorhaben): | R - AI4SAR |
| Standort: | Oberpfaffenhofen |
| Institute & Einrichtungen: | Institut für Hochfrequenztechnik und Radarsysteme Institut für Hochfrequenztechnik und Radarsysteme > Satelliten-SAR-Systeme |
| Hinterlegt von: | Rizzoli, Paola |
| Hinterlegt am: | 15 Apr 2026 12:41 |
| Letzte Änderung: | 15 Apr 2026 12:41 |
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