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
KI:STE project connects with HDS-LEE graduate school
We are glad to announce the admission of the KI:STE doctoral researchers at HDS-LEE (Helmholtz School for Data Science in Life, Earth and Energy).HDS-LEE provides
Introducing Ambrosys
Ambrosys (https://ambrosys.de) is a dynamic company with a strong research and development profile. Our work is focused on software development for artificial intelligence and data
AQ-Bench preprint available for public discussion
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
PhD Project Pitches
KI:STE welcomes the new year by sharing goals and planning further collaboration. KISTE project partners met yesterday to discuss the PhD projects within the different
KI:STE Starting In November
Artificial intelligence (AI) methods are currently experiencing rapid development and are also being used more and more frequently in the context of environmental data. However,
Virtual Kickoff Meeting On 9th Of November
We are excited to meet and kick start our KI:STE project virtually on 9th of November. During the meeting we want to introduce our new
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
Start
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Grant agreement: 67KI2043
