AI Strategy for Earth system data
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.
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.
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
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