KI:STE
AI Strategy for Earth system data
Develop AI methods for environmental data
Build an environmental
AI e-learning platform
Build an environmental
AI platform
About KI:STE
The KISTE project aims to exploit recent developments in artificial intelligence – especially deep learning methods – for sound environmental data analysis. The scientific goal is the implementation of current AI approaches for spatiotemporal variable pattern recognition and pattern analysis in environmental data in the subject areas clouds, snow/ice, water, air quality and vegetation within the framework of five dissertations. In addition, a technical platform will be created to make powerful AI applications on environmental data available in a portable way. An online AI learning platform will be established with Interfaces to this AI platform. This e-learning offer is aimed at the location-independent education of young scientists and other interested parties. It will use the concepts and methods developed in the five research fields as teaching material.
The grant is valid for the period from 01.11.2020 to 31.10.2023.
Work packages
Clouds
Improved predictability of the availability of solar energy through the analysis of spatial-temporal patterns of cloud parameters based on geostationary satellite observations
Snow / Ice
Improved prediction of melting processes in glacial ice by combining meteorological environmental parameters with measurements from in-situ sensors and process simulations, as well as exemplary analysis of the consequences for hydrological runoff models
Water
Improve predictions of droughts or floods by examining extreme hydrometeorological events from the combination of simulation and observation data
Air quality
Air quality: Development of a methodology for seasonal forecasts of extreme values of air pollution by examining the influence of plant health on air quality
Vegetation
Development of multi-task methods to predict several parameters of the plant condition simultaneously, especially with regard to extreme events.
AI lighthouse for the environment, climate, nature and resources
KISTE is part of the initiative “AI light house for the environment, climate, nature and resources”. These are projects that use their digital know-how and creativity to overcome ecological challenges.
Project Partners
HDS-LEE
Latest News
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
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.
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
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
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
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
Get in touch
Phone: + +49 2461 61 6402
Email: s.stadtler [at] fz-juelich.de
Forschungszentrum Jülich GmbH
Institute for Advanced Simulation (IAS)
Jülich Supercomputing Centre (JSC)
Wilhelm-Johnen-Straße
52425 Jülich
Germany
MON-FRI 08:00 - 17:00
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Grant agreement: 67KI2043
