M.Sc. Felix Riese
- Wissenschaftlicher Mitarbeiter
- room: 002 (Geb. 20.52)
- phone: +49 721 608 47304
- fax: +49 721 608 48450
- felix riese ∂does-not-exist.kit edu
Curriculum vitae
| 09/2019 - 12/2019 | Visiting Researcher and Data Scientist at FluroSat, Sydney (AU) |
| 04/2019 | TRUST Measurement Campaign in Peru |
| 09/2018 | "Brain to Market" Summer School in Paris about neuropathology and entrepreneurship (aftermovie) |
| 07/2018 | VISUM Summer School in Porto (PT) about machine learning and computer vision (aftermovie) |
| Since 06/2018 | Participant of the KIT mentoring program X-Ment for researchers |
| Since 01/2018 | MBA fellow at the Collège des ingénieurs (CDI) in Paris, France |
| Since 05/2017 | PhD student at the Institute for Photogrammetry and Remote Sensing of the Karlsruhe Institute of Technology (KIT) |
| 01/2016 - 05/2018 | Lecturer at the Baden-Wuerttemberg Cooperative State University (DHBW) in the degree programm "computer science" (see teaching) |
| 10/2014 - 03/2017 | Master of Science in physics at the KIT, major in "data analysis in particle physics" |
| Masterthesis: "Boosted-Jet Reconstruction Methods in a Search for Higgs-Boson Production in Association with a Top-Quark-Antiquark Pair at the CMS Experiment" at the Institute of Experimental Nuclear Physics (EKP) of the KIT with Prof. Dr. Husemann | |
| 10/2011 - 09/2014 | Bachelor of Science in physics at the KIT |
Research interests
- Machine Learning: Data Analysis, Supervised and Unsupervised Learning, Self-Organizing Maps (SOM), Deep Learning, Python
- Hyperspectral Remote Sensing, Multispectral Satellites (Sentinel-2), UAV
- Geoscience: estimation of soil surface parameters
Teaching
- Introduction to linear algebra, analysis and statistics at the DHBW Karlsruhe
- Introduction to knowledge-based systems at the DHBW Karlsruhe
- Introduction to LaTeX at the DHBW Karlsruhe
Other profiles
- Professional network via LinkedIn
- Code & datasets at GitHub
- Publications at GoogleScholar
- Publications at ResearchGate
Awards
- Best Paper Award at the ISPRS Geospatial Week 2019 in Enschede (NL)
Workshops, talks and posters
| June 2019 | Talk about the publication "Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data" at the ISPRS Geospatial Week in Enschede (NL), Code |
| May 2019 | Talk "Satellite Computer Vision mit Keras und TensorFlow - Best Practices und Beispiele aus der Forschung" at the M3 conference in Mannheim (DE), Slides and Code |
| April 2019 | 2-day Workshop about "Introduction to Artificial Intelligence in Remote Sensing" at the Universidad Nacional Agraria La Molina in Lima (PE) |
| March 2019 | Poster about "Land use classification with remote sensing data and machine learning in the Lima region (Peru)" at the Tag der Hydrologie 2019 (engl. Hydrology Day) in Karlsruhe (DE) |
| February 2019 | Poster about "Land use classification with remote sensing data and machine learning in the Lima region (Peru)" at the GRoW Midterm Conference in Frankfurt am Main (DE) |
| October 2018 | Talk "Satellite data is for everyone: Insights into modern remote sensing research with open data and Python" at the PyCon.DE 2018 in Karlsruhe (DE), Slides and Code and Video of the talk |
| October 2018 | Talk about the publication "Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data" at the ISPRS TCI Symposium 2018 in Karlsruhe (DE) |
| September 2018 | Talk about the publication "Fusion of hyperspectral and ground penetrating radar to estimate soil moisture" at the WHISPERS 2018 conference in Amsterdam (NL) |
| July 2018 | Talk about the publication "Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data" at the IGARSS 2018 conference in Valencia (ES) |
| July 2018 | Poster about "Self-organizing maps for regression with hyperspectral data" at VISUM summer school in Porto (PT) |
| March 2018 | Talk about the publication "Modeling Subsurface Soil Moisture Based on Hyperspectral Data - First Results of a Multilateral Field Campaign" at the DGPF conference in Munich (DE) |
Media and Press
- SWR2 Impuls radio interview about "Artificial Intelligence - Search for drinking water with satellites and AI" with Sina Keller
- Press release by the KIT SEK with the title "From satellite image towards drinking water concepts" about the Peru measurement campaign and the contribution of the IPF in the TRUST project
- Youtube video with the title "Artificial Intelligence in Environmental Research" about my PhD and the contribution of the IPF in the TRUST project (TV broadcast in RegioTV and BadenTV)
- Podcast episode about "Remote Sensing with Multispectral Remote Sensing" of TechTiefen with Nico Kreiling in July 2019
- Youtube video of the talk "Satellite data is for everyone: Insights into modern remote sensing research with open data and Python" at PyCon.DE in October 2018
Publikationen
2021
Sefrin, O.; Riese, F. M.; Keller, S. (2021). Deep Learning for Land Cover Change Detection. Remote sensing, 13 (1), Article no: 78. doi:10.3390/rs13010078
2020
Keller, S.; Riese, F. M.; Allroggen, N.; Jackisch, C. (2020, May). HydReSGeo: Field experiment dataset of surface-sub-surface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques. doi:10.5880/fidgeo.2020.015
Riese, F. M. (2020). LUCAS Soil Texture Processing Scripts. doi:10.5281/zenodo.3871431
Riese, F. M. (2020). Processing Scripts of the ALPACA Dataset. doi:10.5281/zenodo.3871459
Riese, F. M. (2020, March). Hyperspectral Processing Scripts for the HydReSGeo Dataset 1.0.0. doi:10.5281/zenodo.3706418
Riese, F. M. (2020, June 10). Development and Applications of Machine Learning Methods for Hyperspectral Data. PhD dissertation. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000120067
Riese, F. M.; Keller, S. (2020). Supervised, Semi-supervised, and Unsupervised Learning for Hyperspectral Regression. Hyperspectral Image Analysis : Advances in Machine Learning and Signal Processing. Hrsg.: S. Prasad, 187–232, Springer Nature. doi:10.1007/978-3-030-38617-7_7
Riese, F. M.; Keller, S.; Hinz, S. (2020). Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data. Remote sensing, 12 (1), Art. Nr.: 7. doi:10.3390/rs12010007
Riese, F. M.; Schroers, S.; Wienhöfer, J.; Keller, S. (2020, April 9). Aerial Peruvian Andes Campaign (ALPACA) Dataset 2019. doi:10.5445/IR/1000118082
Sefrin, O.; Riese, F. M.; Keller, S. (2020). Code for Deep Learning for Land Cover Change Detection 1.0.1. doi:10.5281/zenodo.4289079
Wagner, P.; Riese, F. M.; Keller, S. (2020). CAOS Sentinel-2 Pipeline in Python. doi:10.5281/zenodo.3879622
2019
Leitloff, J.; Riese, F. M. (2019, May 15). Satellite Computer Vision mit Keras und Tensorflow - Best practices und Beispiele aus der Forschung. Minds Mastering Machines (2019), Mannheim, Germany, May 14–16, 2019. doi:10.5281/zenodo.4056744
Leitloff, J.; Riese, F. M.; Kreiling, N. (2019). Fernerkundung mit multispektralen Satellitenbildern.
León, C. D.; Kosow, H.; Zahumensky, Y.; Krauß, M.; Wasielewski, S.; Minke, R.; Wienhöfer, J.; Riese, F. M.; Keller, S.; Sturm, S.; Brauer, F.; Hügler, M.; Gottwalt, J.; Riepl, D. (2019). Solutions and planning tools for water supply and wastewater management in prosperous regions tackling water scarcity. Proceedings of the GRoW Midterm Conference - Global analyses and local solutions for sustainable water resources management, Frankfurt am Main, 20-21 February 2019. Ed.: A. Kramer, 28–31, adelphi.
Riese, F. M. (2019). Processing Scripts for Thermal Infrared Cameras. doi:10.5281/zenodo.3576242
Riese, F. M. (2019). SUSI: SUpervised Self-organIzing maps in Python. Zenodo. doi:10.5281/zenodo.2609130
Riese, F. M. (2019). CNN Soil Texture Classification. Zenodo. doi:10.5281/zenodo.2540718
Riese, F. M.; Keller, S. (2019). Hyperspectral Regression: Code Examples. doi:10.5281/zenodo.3450676
Riese, F. M.; Keller, S. (2019). SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python.
Riese, F. M.; Keller, S. (2019). Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2019 ISPRS) Geospatial Week 2019, Enschede, NL, June 10-14, 2019. Vol. IV-2/W5, 615–621. doi:10.5194/isprs-annals-IV-2-W5-615-2019
Riese, F. M.; Sexauer, A.; Wetzel, C.; Reiling, Y.; Keller, S.; Hinz, S. (2019). Künstliche Intelligenz in der Umweltforschung.
Sefrin, O.; Riese, F. M.; Keller, S. (2019). Soil Texture Processing. doi:10.5281/zenodo.3431628
2018
Keller, S.; Maier, P.; Riese, F.; Norra, S.; Holbach, A.; Börsig, N.; Wilhelms, A.; Moldaenke, C.; Zaake, A.; Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International journal of environmental research and public health, 15 (9), 1881/1–15. doi:10.3390/ijerph15091881
Keller, S.; Riese, F. M.; Allroggen, N.; Jackisch, C.; Hinz, S. (2018). Modeling Subsurface Soil Moisture Based on Hyperspectral Data : First Results of a Multilateral Field Campaign. Photogrammetrie, Fernerkundung, Geoinformatik, Kartographie 2018 (PFGK18) : Beiträge der 37. Wissenschaftlich-Technische Jahrestagung der DGPF e.V., 5. Münchner GI-Runde Runder Tisch GIS e.V. und des 66. Deutscher Kartographie Kongress der DGfK e.V., München, Deutschland, 7. - 9. März 2018. Hrsg.: T. P. Kersten, 34–48, Deutsche Gesellschaft für Photogrammetrie.
Keller, S.; Riese, F. M.; Stötzer, J.; Maier, P. M.; Hinz, S. (2018). Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences - Symposium “Innovative Sensing – From Sensors to Methods and Applications”, Karlsruhe, Germany, 10–12 October 2018. Volume: IV-1, 101–108, ISPRS. doi:10.5194/isprs-annals-IV-1-101-2018
Leitloff, J.; Riese, F. M. (2018). Examples for CNN training and classification on Sentinel-2 data. doi:10.5281/zenodo.3268451
Riese, F. M.; Keller, S. (2018). Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22nd - 27th July, 2018, 6151–6154, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IGARSS.2018.8517812
Riese, F. M.; Keller, S. (2018, April 24). Hyperspectral benchmark dataset on soil moisture. doi:10.5281/zenodo.1227837
Riese, F. M.; Keller, S. (2018). Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture. 9th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (Whispers 2018), Amsterdam, NL, September 23-26, 2018, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WHISPERS.2018.8747076
Riese, F. M.; Leitloff, J. (2018). Satellite data is for everyone: Insights into modern remote sensing research with open data and Python. doi:10.5281/zenodo.4056516