Recent Articles

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they …
Furkan M. Torun, Sebastian Virreira Winter, Sophia Doll, Felix Riese, Artem Vorobyev, Johannes B. Mueller-Reif, Philipp E. Geyer, Maximilian T. Strauss
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Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Using patient-reported quality of life outcomes to assess the validity of smartphone-based remote assessment of motor and cognitive performance in manifest Huntington’s disease patients
Objective: Explore associations between remote monitoring of cognitive and motor functions and digitally administered, self-assessed …
Felix Riese, Johannes Rennig, Florian Lipsmeier, Atieh Bamdadian, Scott Schobel, Christian Gossens, Patrick Weydt, Jonas Dorn, Edward Wild, Dylan Trundell, Cedric Simillion, Michael Lindemann
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Integrated Water Management Solutions in the Lurín Catchment, Lima, Peru – Supporting United Nations’ Sustainable Development Goal 6. Final report of the joint project TRUST
Final report of the joint project TRUST “Sustainable, fair and environmentally sound drinking water supply for prosperous regions with …
Jan Bondy, Friederike Brauer, Jaime Cardona, Johannes Chamorro, Thilo Fischer, Lucia Hahne, Stefan Hinz, Michael Hügler, Christian D. León, Sina Keller, Hannah Kosow, Hanna Kramer, Manuel Krauss, Ralf Minke, Fabienne Minn, Felix Riese, Samuel Schroers, Stefan Stauder, Sebastian Sturm, Stephan Wasielewski, Jan Wienhöfer, Yvonne Zahumensky
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Integrated Water Management Solutions in the Lurín Catchment, Lima, Peru – Supporting United Nations’ Sustainable Development Goal 6. Final report of the joint project TRUST
Deep Learning for Land Cover Change Detection
Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and …
Oliver Sefrin, Felix Riese, Sina Keller
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Deep Learning for Land Cover Change Detection
Development and Applications of Machine Learning Methods for Hyperspectral Data
Hyperspectral remote sensing of the Earth relies on data from passive optical sensors that are mounted on platforms such as satellites …
Felix Riese
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Development and Applications of Machine Learning Methods for Hyperspectral Data
HydReSGeo: Field experiment dataset of surface-sub-surface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques
This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, …
Sina Keller, Felix Riese, Niklas Allroggen, Conrad Jackisch
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HydReSGeo: Field experiment dataset of surface-sub-surface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques
Supervised, Semi-supervised, and Unsupervised Learning for Hyperspectral Regression
In this chapter, we present an entire workflow for hyperspectral regression based on supervised, semi-supervised, and unsupervised …
Felix Riese, Sina Keller
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Supervised, Semi-supervised, and Unsupervised Learning for Hyperspectral Regression
Aerial Peruvian Andes Campaign (ALPACA) Dataset 2019
The Aerial Peruvian Andes Campaign (ALPACA) dataset was acquired during a measurement campaign of the Institute of Photogrammetry and …
Felix Riese, Samuel Schroers, Jan Wienhöfer, Sina Keller
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Aerial Peruvian Andes Campaign (ALPACA) Dataset 2019
KI im Dienst der Nachhaltigkeit: Trinkwasser für Peru, Precision Farming in Australien
Trinkwasser und Nahrung – diese Grundbedürfnisse des Menschen sind in weiten Teilen der Welt keine Selbstverständlichkeit. Künstliche …
Felix Riese
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KI im Dienst der Nachhaltigkeit: Trinkwasser für Peru, Precision Farming in Australien
Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data
Machine learning approaches are valuable methods in hyperspectral remote sensing, especially for the classification of land cover or …
Felix Riese, Sina Keller, Stefan Hinz
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Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on …
Felix Riese, Sina Keller
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Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
Solutions and planning tools for water supply and wastewater management in prosperous regions tackling water scarcity
Innovative solutions and planning tools for safe drinking water supply and sustainable wastewater management are the focus of the TRUST …
Christian D León, Hannah Kosow, Yvonne Zahumensky, Manuel Krauß, Stephan Wasielewski, Ralf Minke, Jan Wienhöfer, Felix Riese, Sina Keller, Sebastian Sturm, Friederike Brauer, Michael Hügler, Jonas Gottwalt, David Riepl
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Solutions and planning tools for water supply and wastewater management in prosperous regions tackling water scarcity
Examples for CNN training and classification on Sentinel-2 data
Overview about state-of-the-art land-use classification from satellite data with CNNs based on an open dataset.
Jens Leitloff, Felix Riese
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Examples for CNN training and classification on Sentinel-2 data
Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR …
Sina Keller, Felix Riese, Johanna Stötzer, Philipp M. Maier, Stefan Hinz
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Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data
Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture
In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) …
Felix Riese, Sina Keller
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Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture
Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae, and Turbidity
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for …
Sina Keller, Philipp M. Maier, Felix Riese, Steffen Norra, Andreas Holbach, Nicolas Börsig, Andre Wilhelms, Christian Moldaenke, Andre Zaake, Stefan Hinz
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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae, and Turbidity
Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data
In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework …
Felix Riese, Sina Keller
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Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data
Modeling Subsurface Soil Moisture Based on Hyperspectral Data: First Results of a Multilateral Field Campaign
Soil moisture dynamics and its spatial distribution represent important data of a landscape. Until today, a precise and spatially …
Sina Keller, Felix Riese, Niklas Allroggen, Conrad Jackisch, Stefan Hinz
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Modeling Subsurface Soil Moisture Based on Hyperspectral Data: First Results of a Multilateral Field Campaign