Explainable Machine Learning
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data.
An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data.
A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. To approach explainable machine learning in the context of natural sciences, 3 core elements must be considered: transparency, interpretability, explainability.
Institute for Numerical Simulation, University of Bonn
Department of Electrical and Computer Engineering, University of Massachusetts Amherst
R. Roscher, B. Bohn, M. F. Duarte and J. Garcke, “Explainable Machine Learning for Scientific Insights and Discoveries,” in IEEE Access, vol. 8, iss. 1, pp. 42200-42216, 2020.
Machine Learning for Plant Phenotyping
Applications such as yield estimation and forecasting are of special interest in agriculture. Consequently, it is important for both scientists and farmers to study traits of the crop and the reaction to different stress factors. To this end, understanding the plant’s phenotype, i.e., the result of environmental influences such as abiotic and biotic stress factors occurring during plant’s development and growth on its genome, is essential.
All partners in the Cluster of Excellence PhenoRob
Julius Kühn-Institut, Siebeldingen
L. Zabawa, A. Kicherer, L. Klingbeil, A. Milioto, R. Töpfer, H. Kuhlmann, and R. Roscher, “Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks,” in CVPR Workshop on Computer Vision Problems in Plant Phenotyping, 2019.
A. Kicherer, R. Roscher, K. Herzog, W. Förstner, and R. Töpfer, “Image based Evaluation for the Detection of Cluster Parameters in Grapevine,” in Acta horticulturae, 2014.
R. Roscher, K. Herzog, A. Kunkel, A. Kicherer, R. Töpfer, and W. Förstner, “Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields,” Computers and Electronics in Agriculture, vol. 100, pp. 148-158, 2014.
Machine Learning for Plant Disease Detection
With a limited amount of arable land, increasing demand for food induced by growth in population can only be meet with more effective crop production and more resistant plants. Crop science is an important research field as it is concerned with the aspect of providing a sufficient amount of food – now and in the future. Since crop plants are exposed to many different stress factors, it is relevant to investigate those factors as well as their behavior and reactions. One of the most severe stress factors are diseases, resulting in a high loss of cultivated plants. In our research, we exploit various machine learning methods to solve application tasks such as forecasting and detection.
INRES-Pflanzenkrankheiten und Pflanzenschutz, University of Bonn
Julius Kühn-Institut, Siebeldingen
L. Strothmann, U. Rascher, and R. Roscher, “Detection of anomalous grapevine berries using all-convolutional autoencoders,” in International Geoscience and Remote Sensing Symposium, 2019.
A. Foerster, J. Behley, J. Behmann, and R. Roscher, “Hyperspectral plant disease forecasting using generative adversarial networks,” in International Geoscience and Remote Sensing Symposium, 2019.
R. Roscher, J. Behmann, A. -K. Mahlein, J. Dupuis, H. Kuhlmann, and L. Plümer, “Detection of Disease Symptoms on Hyperspectral 3D Plant Models,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, pp. 89-96.
R. Roscher, J. Behmann, A. -K. Mahlein, and L. Plümer, “On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models,” in Workshop on Hyperspectral Image and Signal Processing, 2016.
R. Roscher, L. Drees, and S. Wenzel, “Sparse representation-based archetypal graphs for spectral clustering,” in IEEE International Geoscience and Remote Sensing Symposium, 2017.
Neural Networks for Analyzing Earth Observation Data
Global climate change plays an essential role in our daily life and is one of the most important topics, nowadays. Thus, the understanding, monitoring and prediction is essential to overcome related challenges. Neural networks are a powerful mean to solve tasks such as classification, detection and regression, where they show promising results reaching accuracies superior to classical and shallow state-of-art machine learning algorithms. However, so far they have been barely explored in the context of Earth observation data. In our research, we use the current advances in the deep learning area and adapt the methods for several applications in remote sensing.
Satellite Geodesy Group, University of Bonn
A. Braakmann-Folgmann, R. Roscher, S. Wenzel, B. Uebbing, and J. Kusche, “Sea level anomaly prediction using recurrent neural networks,” in Proc. Conference on Big Data from Space, 2017.
K. Franz, R. Roscher, A. Milioto, S. Wenzel, and J. Kusche. “Ocean Eddy Identification and Tracking using Neural Networks.” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018.
R. Roscher, B. Uebbing, and J. Kusche, “STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields,” Remote Sensing of Environment, vol. 201, pp. 148-164, 2017.
Deep Self-taught learning
Self-taught learning (STL) has become a promising paradigm to exploit large amounts of unlabeled data for feature learning and classification. It utilizes both labeled and unlabeled data without the requirement that both sets have to share the same distribution and the same land use and land cover classes. In our research, we analyze the applicability of STL for the interpretation of remote sensing images. Moreover, we combine STL and the concepts of deep learning to learn valuable feature representations for remote sensing image classification.
A. Bettge, R. Roscher, and S. Wenzel, “Deep self-taught learning for remote sensing image classification,” in Proc. Conference on Big Data from Space, 2017.
R. Roscher, S. Wenzel, and B. Waske, “Discriminative archetypal self-taught learning for multispectral landcover classification,” in IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), 2016.
R. Roscher, C. Römer, B. Waske, and L. Plümer, “Landcover classification with self-taught learning on archetypal dictionaries,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 2358-2361.