Felix M. Riese

596 total citations
14 papers, 358 citations indexed

About

Felix M. Riese is a scholar working on Artificial Intelligence, Media Technology and Environmental Engineering. According to data from OpenAlex, Felix M. Riese has authored 14 papers receiving a total of 358 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Media Technology and 4 papers in Environmental Engineering. Recurrent topics in Felix M. Riese's work include Soil Geostatistics and Mapping (4 papers), Remote-Sensing Image Classification (4 papers) and Soil Moisture and Remote Sensing (3 papers). Felix M. Riese is often cited by papers focused on Soil Geostatistics and Mapping (4 papers), Remote-Sensing Image Classification (4 papers) and Soil Moisture and Remote Sensing (3 papers). Felix M. Riese collaborates with scholars based in Germany and Denmark. Felix M. Riese's co-authors include Sina Keller, Stefan Hinz, Christian Moldaenke, Stefan Norra, Andreas Holbach, Sophia Doll, Johannes B. Mueller‐Reif, Maximilian T. Strauss, Sebastian Virreira Winter and Philipp E. Geyer and has published in prestigious journals such as International Journal of Environmental Research and Public Health, Remote Sensing and Journal of Proteome Research.

In The Last Decade

Felix M. Riese

13 papers receiving 349 citations

Peers

Countries citing papers authored by Felix M. Riese

Since Specialization

Citations

This map shows the geographic impact of Felix M. Riese's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Felix M. Riese with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix M. Riese more than expected).

Fields of papers citing papers by Felix M. Riese

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Felix M. Riese. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Felix M. Riese. The network helps show where Felix M. Riese may publish in the future.

Co-authorship network of co-authors of Felix M. Riese

This figure shows the co-authorship network connecting the top 25 collaborators of Felix M. Riese. A scholar is included among the top collaborators of Felix M. Riese based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Felix M. Riese. Felix M. Riese is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

#	Work	Indexed citations
1	SuSi: SUpervised Self-organIzing maps in Python 2025 ·Zenodo (CERN European Organization for Nuclear Research) ·Felix M. Riese	0
2	Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data 2022 ·Journal of Proteome Research ·(unknown), Sebastian Virreira Winter, Sophia Doll, Felix M. Riese, (unknown), Johannes B. Mueller‐Reif, Philipp E. Geyer, Maximilian T. Strauss	32
3	Development and Applications of Machine Learning Methods for Hyperspectral Data 2020 ·Repository KITopen (Karlsruhe Institute of Technology) ·Felix M. Riese	2
4	Deep Learning for Land Cover Change Detection 2020 ·Remote Sensing ·(unknown), Felix M. Riese, Sina Keller	96
5	CNN Soil Texture Classification 2020 ·Zenodo (CERN European Organization for Nuclear Research) ·Felix M. Riese	2
6	felixriese/hyperspectral-processing: Hyperspectral Processing Scripts for the HydReSGeo Dataset 1.0.0 2020 ·Zenodo (CERN European Organization for Nuclear Research) ·Felix M. Riese	1
7	jensleitloff/CNN-Sentinel: Examples for CNN training and classification on Sentinel-2 data – Version 1.1 2020 ·Zenodo (CERN European Organization for Nuclear Research) ·Jens Leitloff, Felix M. Riese	2
8	Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data 2019 ·Remote Sensing ·Felix M. Riese, Sina Keller, Stefan Hinz	66
9	Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data 2019 ·Repository KITopen (Karlsruhe Institute of Technology) ·Felix M. Riese, Sina Keller	46
10	Is it possible to retrieve soil-moisture content from measured VNIR hyperspectral data? 2018 ·arXiv (Cornell University) ·Sina Keller, Felix M. Riese, (unknown), (unknown), Stefan Hinz	1
11	Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data 2018 ·Repository KITopen (Karlsruhe Institute of Technology) ·Sina Keller, Felix M. Riese, (unknown), (unknown), Stefan Hinz	9
12	Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data 2018 ·Felix M. Riese, Sina Keller	25
13	Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity 2018 ·International Journal of Environmental Research and Public Health ·Sina Keller, (unknown), Felix M. Riese, Stefan Norra, Andreas Holbach, (unknown), (unknown), Christian Moldaenke, (unknown), Stefan Hinz	74
14	Modeling Subsurface Soil Moisture Based on Hyperspectral Data : First Results of a Multilateral Field Campaign 2018 ·Sina Keller, Felix M. Riese, Niklas Allroggen, Conrad Jackisch, Stefan Hinz	2

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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