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.
Open Positions
Currently, we do not have open positions.
News
2023-12: The Machine Learning for Remote Sensing Workshop, co-organized by Ribana, is accepted at ICLR
2023-11: The Workshop on Machine Vision for Earth Observation and Environment Monitoring, is happening at BMVC
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 year’s 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: Eike attends the EGU General Assembly 2023 in Vienna.
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