Using atmospheric understanding to exploit multi-spectral images
Ankit Patnala published his first paper in the IEEE journal. There is yet more information currently unused by machine learning in satellite data. Natural images come in RGB and thus…
Can Deep Learning close the Gap between terrestrial modeled Data and Observations?
Within the HDS-LEE graduate school, Kaveh P. Yousefi presented his U-Net capable of improving model-based precipitation and surface pressure fields towards observations.
Jungle-Net Poster presentation
Within the HDS-LEE graduate school and the ISPRS congress Timo Stomberg presented his Jungle-Net capable of giving insights into wilderness using explainable machine learning.
KI:STE Project Meeting
The KI:STE consortium meets on the 15th and 16th of November to exchange their latest scientific results, get insights to the newest functionalities of the AI platform and finally inaugurate…
Lecture on Explainable Machine Learning for Earth Science
The live stream of Prof. Dr. Ribana Roscher's lecture on explainable machine learning can be found here on YouTube. The lecture has two parts. The first part introduces all you…
KI:STE OpenGeoHub Summer School 2022
Following a long tradition of OpenGeoHub Summer Schools, the KI:STE project took the opportunity to co-host this year's Summer School on Earth system data analysis. Ribana Roscher, Benedikt Gräler, Markus…
Explainable Machine Learning for Air Quality
The article "Explainable machine learning reveals capabilities, reundancy, and limitations of a geospatial air quality benchmark dataset" is now available in the journal Machine Learning and Knowledge Extraction. Here you…
KI:STE Science Meeting
On the 9th and 10th of February the KI:STE researchers meet for scientific discussions. After presenting the latest progress and results to each other we take time to discuss and…
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
The preprint of the article “Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties” is now available here: https://doi.org/10.5194/gmd-2022-2 This work is a follow-up collaboration between…
CESOC
The center for Earth system observation and computational analysis (CESOC) is a research partnership founded in October 2020 between the Universities of Bonn and Cologne as well as the Forschungszentrum…