Deep Learning for Remote Sensing

Felix Riese
December 2020
The rights of the image belong to Dr. Felix Riese.
Archived Project
Felix Riese
Felix Riese
Head of Product

Head of Product at Peter Park.

Publications

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
PDF Cite Code Project DOI
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
PDF Cite Project DOI
Development and Applications of Machine Learning Methods for Hyperspectral Data
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
PDF Project Source Document
KI im Dienst der Nachhaltigkeit: Trinkwasser für Peru, Precision Farming in Australien
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
Cite Code Project Video Talk
Examples for CNN training and classification on Sentinel-2 data

Events

Workshop talk about "Data Science - from Environmental Sciences to Medical Diagnostics"
Talk about the transition from academia to the industry in data science.
December 2022 Karlsruhe
Felix Riese
Project Project
Workshop talk about "Data Science - from Environmental Sciences to Medical Diagnostics"
Hyperspectral Estimation Framework: Selected Applications of Machine Learning
Presentation at the Karlsruhe Institute of Technology (KIT) as status update of my current Ph.D. studies.
February 2020 Karlsruhe (DE)
Felix Riese
Project
Hyperspectral Estimation Framework: Selected Applications of Machine Learning
Podcast episode about 'Fernerkundung mit multispektralen Satellitenbildern'
We were part of the German TechTiefen podcast with Nico Kreiling about multispectral satellite computer vision. We explain our attempt on sustainability research in regards to water quality and land use detection.
July 2019 Karlsruhe (DE)
Jens Leitloff, Felix Riese
Project Podcast
Podcast episode about 'Fernerkundung mit multispektralen Satellitenbildern'
Talk about the publication 'Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data'
I have presented our publication ‘Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data’ at the ISPRS Geospatial Week in an oral talk.
June 2019 — June 2019 Enschede (NL)
Felix Riese
Code Project Project Publication Best Paper
Talk about the publication 'Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data'
Satellite Computer Vision mit Keras und Tensorflow - Best Practices und Beispiele aus der Forschung
Im Forschungsfeld des Maschinellen Lernens werden zunehmend leicht zugängliche Framework wie Keras, Tensorflow oder Pytorch verwendet. …
May 2019 — May 2019 Mannheim (DE)
Jens Leitloff, Felix Riese
Code Project Slides
Satellite Computer Vision mit Keras und Tensorflow - Best Practices und Beispiele aus der Forschung
Land Use Classification with Remote Sensing Data and Machine Learning in the Lima Region (Peru)
Poster presentation at the Tag der Hydrologie (Hydrology Day) at the Karlsruhe Institute of Technology (KIT).
March 2019 Karlsruhe (DE)
Felix Riese, Sina Keller
Project Poster
Satellite data is for everyone: insights into modern remote sensing research with open data and Python
The largest earth observation programme Copernicus (http://copernicus.eu) makes it possible to perform terrestrial observations …
October 2018 — October 2018 Karlsruhe (DE)
Jens Leitloff, Felix Riese
PDF Code Project Video
Satellite data is for everyone: insights into modern remote sensing research with open data and Python