Welcome to the website of the Data Science for Crop Systems Group. Our research aims at the development of machine learning methods, which are particularly designed for the analysis of remote sensing data. We specifically focus on techniques for sophisticated feature learning and data analysis methods which integrate prior knowledge such as scientific domain knowledge. We believe that it is important to develop methods which ensure a high discrimination power and at the same time model the underlying structure of the data. A particular research direction is explainable machine learning approaches which are able to tackle common challenges in the sciences such as the provision of reliable and scientific consistent results. These models give us a deeper understanding of what we have learned and can provide us with new scientific insights.
Open Position
Task: Bee detection and tracking in images and videos.
Requirements: 1) Bachelor’s Degree, 2) Enrolled at a German university, 3) Knowledge of Python, 4) Ability to work independently with deep learning and video analysis.
Contact: Jana
Task: Pre-processing of plant/agricultural datasets for deep learning purposes (no labeling!)
Requirements: Enrolled at a German university, Basic knowledge of Python or ArcGIS/QGIS is desirable
Period: asap – 31.10.23 (8h/week)
Contact: Lukas
News
2023-02: Lukas presents his work at the doctoral seminar of our Institute of Geodesy and Geoinformation
2023-01: Jana starts in the new project TrAgS
2022-10: Our dataset GrowliFlower is finally published and publicly available.
2022-10: Burak Ekim visits our group for 2 weeks
2022-09: Ribana and Jana attend the EIP-Agri OG-Workshop in Hannover and present our project OPTIKO
2022-07: First learn, then do sports: Lukas @ amazing Vision and Sports Summer School in Prague.
2022-07: Eike presents the paper ,,Occlusion Sensitivity Analysis of Neural Network Architectures for Eddy Detection” remotely at IGARSS 2022
2022-06: The whole group is attending and presenting at the ISPRS Congress in Nice
2022-05: Eike presents our current work on machine learning-based identification and classification of ocean eddies at the Living Planet Symposium 2022 as well as remotely at the EGU General Assembly 2022
2022-04: The preprint of our paper ,,GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER” is out now
2022-04: Johannes joins our team as a doctoral student
Current Projects
for Sustainable Crop Production
of cauliflower cultivation
Disentangling the Role of Land Use and Water Management
MapInWild:
Mapping and Interpreting Wilderness from Space