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
Ambrosys
Latest News
What is your research about?
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
Atmospheric Water Cycle and Remote Sensing
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)
Meet 52°North!
52°North is devoted to spatial information research. As a non-profit company in Münster, we support open science through open data and open source software. Our
How can explainable AI be used to understand what constitutes wilderness?
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
#vEGU21
We were glad to present the KI:STE project and the AI-Platform prototype at the virtual European Geoscience Union General Assembly 2021! “This year vEGU21 has
Geohazards modeling in Göttingen and Aachen
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
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
