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 that integrate prior knowledge, such as scientific domain knowledge. We believe it is important to develop methods that ensure a high discrimination power and simultaneously 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.
See also our propositions for Bachelor and Master theses!
2023-06: Our paper ,,Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower” was accepted in IEEE Geoscience and Remote Sensing Letters and finally published.
2023-06: Jana presents her work at this years summer doctoral seminar of our Institute of Geodesy and Geoinformation
2023-05: Our Ph.D. students attend this year’s PhenoRob Career Fair.
2023-04: MapInWild article has been published.
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
2022-06: The whole group is attending and presenting at the ISPRS Congress in Nice
2022-04: The preprint of our paper ,,GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER” is out now
Mapping and Interpreting Wilderness from Space