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…
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…
If you are interested in the talk Scarlet Stadtler gave at CASUS, please check out this video here https://youtu.be/j1DLOoi0N5Y CASUS Institute Seminar, Dr. Scarlet Stadtler, Forschungszentrum Jülich GmbH/Jülich Supercomputing Centre (JSC)…
There was a short article published in the "effzett" about KI:STE PI Scarlet Stadtler. It can be found here: https://effzett.fz-juelich.de/2-21/woran-forschen-sie-gerade (in German) You can find out more about Scarlet Stadtler…
General Introduction The Institute for Geophysics and Meteorology at the University of Cologne is involved in geophysical exploration (from solar system, extrasolar planets, and moons) to our earth system's atmosphere…
On July 8th Timo Stomberg, KI:STE doctoral researcher, presented jUngle-Net at the ISPRS Congress 2021. jUngle-Net is an explainable neural network which we use to gain new insights into the…
Our contribution to the KI:STE project combines the geohazards modeling expertise of the Computational Geoscience group at the Geoscience Centre at Georg-August University of Göttingen led by Prof. Julia Kowalski,…
The preprint of the article “AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics” by Clara Betancourt et al. is now in the public discussion phase. Creating…