- Autonomous field management – An enabler of sustainable future in agriculture. Agricultural Systems 206, 2023, 103607 more… Full text ( DOI )
- Smart Rural Areas Data Infrastructure (SRADI) – an information logistics framework for digital agriculture based on open standards. 41. GIL-Jahrestagung 2021 - Fokus: Informations- und Kommunikationstechnologie in kritischen Zeiten, Gesellschaft für Informatik e.V. (GI), 2021, 109-114 more…
- Towards a Distributed Digital Twin of the Agricultural Landscape. Journal of Digital Landscape Architecture (5), 2020 more… Full text ( DOI )
David Gackstetter, M.Sc.
Biography
David Gackstetter earned his bachelor's degree in Environmental and Resource Management from Brandenburg University of Technology (BTU) Cottbus-Senftenberg with a focus on environmental modeling. In 2019, he completed his Master's degree in Environmental Engineering (TUM), focusing on the application of machine learning in transportation engineering. Since then, he has been working at the TUM Central Institute Hans Eisenmann Forum for Agricultural Sciences (HEF) in parts as a project engineer on the development of data infrastructures to support agricultural science research at the Weihenstephan campus. Furthermore, he also works there as a Science Manager, in the context of which he initiates, supports and coordinates interdisciplinary projects in the thematic area of Digital Agriculture. Since 2022, he has been working on his doctorate together with the Chair of Remote Sensing Methodology and the Assistant Professorship for Precision Agriculture.
Focus and interests
- Deep learning in remote sensing and earth observation
- Fusion of multimodal, multitemporal and multispectral data
- Meta Learning
- Application of machine learning methods in agricultural and environmental sciences