Industrial Data Science
Forschungsinteressen und -themen
- Modeling and analysis of multimodal complex data
- Environmental monitoring
- Industrial data science with focus on agricultural technology and food industry
Mitglieder
- Martin Atzmüller
- Paul Breiding
- Thom Jarmer
- Kai-Uwe Kühnberger
- Tim Römer (Sprecher)
- Matthias Rottmann
- Björn Waske
KI-Reallabor Agrar Volkswagen Stiftung/zukunft.niedersachsen
Joint Lab Künstliche Intelligenz & Data Science (2023-2028)
HybrInt, Volkswagen Stiftung/zukunft.niedersachsen (Semantische Informationssysteme) (2023-2026)
Sustainable Silk Road 4.0 (AG Fernerkundung und Digitale Bildverarbeitung) (2023-2025)
FRED Frischdatenmanagement von Farm to Fork in der Edge, BMWK (Semantische Informationssysteme) (2022-2025)
2025
Multi-modal vision transformer for high resolution soil texture prediction of German agricultural soils using remote sensing imagery.
Wittstruck, L., Waske, B., Jarmer, T., 2025, Remote Sensing of Environment, 331: 114985.
Comparative analysis of UAV-based LiDAR and photogrammetric systems for the detection of terrain anomalies in a historical conflict landscape
Storch, M., Kisliuk, B., Jarmer, T., Waske, de Lange, N., 2025, Science of Remote Sensing, 11, 100191.
Enhancing Convolutional Block Attention with Self-Attention on Agricultural Image Classification. Arnab Ghosh Chowdhury, Amos Smith, Martin Atzmueller, 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI).
BLENDgänger: Generating Synthetic MBES Data for Underwater UXO Perception Tasks. Amos Smith, Nael Jaber†, Leif Christensen, Martin Atzmueller. In OCEANS 2025 - Great Lakes, S. 1-9.
DOI:10.23919/OCEANS59106.2025.11245155