David Gackstetter

David Gackstetter, M.Sc., is a Researcher at the Technical University of Munich (TUM) within the Central Institute Hans Eisenmann Forum for Agricultural Sciences (HEF). He holds roles as a Project Engineer and Science Manager, focusing on data infrastructure development and interdisciplinary project coordination in Digital Agriculture. He is also pursuing a doctoral degree with the Chair of Remote Sensing Methodology and the Assistant Professorship for Precision Agriculture.

Education: B.Sc. in Environmental and Resource Management from BTU Cottbus-Senftenberg (focus: environmental modeling), M.Sc. in Environmental Engineering from TUM (focus: machine learning in transportation engineering).

Research interests include deep learning applications in remote sensing and Earth observation, fusion of multimodal/multitemporal data, meta-learning, and machine learning in agricultural/environmental sciences. Recent publications explore autonomous field management, crop type mapping, and digital twin frameworks for agricultural landscapes.

Publications highlight contributions to FAIR data principles in agriculture, IoT interoperability, and distributed digital twin systems. No scientific awards are explicitly mentioned. He coordinates projects like SRADI and FAIRagro, contributing to data infrastructure and open standards in digital agriculture. His work integrates sensor networks and machine learning to address sustainability and precision agriculture challenges.

He is affiliated with the Chair of Remote Sensing Technology at TUM, collaborating on projects involving satellite imagery analysis and agricultural data systems.