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
2022-04: Johannes joins our team as a doctoral student
2022-03: Ahmed joins our team as a doctoral student
2022-02: Acceptance of our paper ,,Behind the leaves: Estimation of occluded grapevine berries with conditional generative adversarial networks.” in the journal Frontiers in Artificial Intelligence
2022-01: Dr. Scarlet Stadtler joins our team as Visiting Researcher
2021-11: Funding of new Collaborative Research Center “Regional Climate Change: The Role of Land Use and Water Management” with participation of our group for Land Use and Land Cover (LULC) classification
2021-11: Funding of new project WIKI: Re-analysis/prediction of water storage in Europe using AI
Remote sensing observations play an important role in the geo- and bioscientific community, since they enable various applications to accurately monitor the Earth and its changes – close-range as well as from space. Observation systems regularly provide data with a spectrally, spatially and temporally high resolution. Beside the challenge to deal with large amounts of data and limited class label information, current and future challenges comprise the definition and the way of integration of scientific domain knowledge, the learning of sophisticated features and the fusion of multiple types of sensor data.
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. Moreover, to take the next step in understanding geo- and biophysical phenomena, both data science models and theory-based models need to be combined to form powerful informed machine learning models. Our research also addresses 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.
Mapping and Interpreting Wilderness from Space