data management
Satellite retrievals
Data driven optimization
Physics based optimization
Cloud system evolution
Human vs Machine labels
Transfer learning
Generalization
Solar energy
Cloud Observations
Weather Forecasting
decision trees
uncertainties
hazard mapping
Landslides
Random Forest
Classification
Geohazards
pattern recognition
interpretable machine learning
explainable machine learning
storage systems
data science
large-scale data analysis
large-scale data management
Design patterns
Stability
Remote Sensing & Imaging
Land Cover & Mapping
Data Processing
ASOS
Self-Supervised Learning
Interpretability & Analysis
data management
Satellite retrievals
Data driven optimization
Physics based optimization
Cloud system evolution
Human vs Machine labels
Transfer learning
Generalization
Solar energy
Cloud Observations
Weather Forecasting
decision trees
uncertainties
hazard mapping
Landslides
Random Forest
Classification
Geohazards
pattern recognition
interpretable machine learning
explainable machine learning
storage systems
data science
large-scale data analysis
large-scale data management
Design patterns
Stability
Remote Sensing & Imaging
Land Cover & Mapping
Data Processing
ASOS
Self-Supervised Learning
Interpretability & Analysis

Learn what AI driven en­viron­mental data analysis can do.

The KISTE project aims to exploit recent developments in artificial intelligence – especially deep learning methods – for sound environmental data analysis.

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Kaveh Patakchi Yousefi
•
PD Dr. Martin G. Schultz

AI for weather modeling

Explore deep learning applications in weather modeling, learn about merging atmospheric model results and observations, and understand how U-Net Convolutional Neural Network (CNN) can be used to improve precipitation predictions.

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1h : 13min
Scarlet Stadtler
•

AI methods

Explore AQ-Bench, understand neural networks and random forests, and acquire tools to assess prediction quality. Synthesize insights on models and datasets for a holistic understanding. Participate in a concise exploration of ML model representation and dataset analysis.

Start Course
18 min
Ann-Kathrin Edrich
•
Dr. Anil Yildiz

AI for geohazards

This course offers an introductory exploration of AI methods in Geohazards research with a special focus on machine learning-based landslide susceptibility mapping

Start Course
1h:09min
Ankit Patnala
•
Ribana Roscher

AI for landcover classification

Dive into remote sensing images, benchmark datasets, contrastive self-supervised learning, and explainable machine learning for Landcover Classification. Discover geospatial datasets in remote sensing and learn handling datasets for effective remote sensing application.

Start Course
1h : 14min

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ASOS
Remote Sensing & Imaging
Data Processing
Ankit Patnala
•
Ribana Roscher
•
Timo Stomberg

AI for landcover classification

Dive into remote sensing images, benchmark datasets, contrastive self-supervised learning, and explainable machine learning for Landcover Classification. Discover geospatial datasets in remote sensing and learn handling datasets for effective remote sensing application.

Start Course
1h : 14min
ASOS
Land Cover & Mapping
Stability
Interpretability & Analysis
Scarlet Stadtler
•
•

AI methods

Explore AQ-Bench, understand neural networks and random forests, and acquire tools to assess prediction quality. Synthesize insights on models and datasets for a holistic understanding. Participate in a concise exploration of ML model representation and dataset analysis.

Start Course
18 min
Self-Supervised Learning
Land Cover & Mapping
Ann-Kathrin Edrich
•
Dr. Anil Yildiz
•
Prof. Dr. Julia Kowalski

AI for geohazards

This course offers an introductory exploration of AI methods in Geohazards research with a special focus on machine learning-based landslide susceptibility mapping

Start Course
1h:09min
Interpretability & Analysis
Design patterns
large-scale data management
large-scale data analysis
data science
PD Dr. Martin Schultz
•
•

Computational aspects of AI for environmental sciences

Understanding how to deal with Earth system data is a must for environmental data scientists. Here, you learn the basics and important modern concepts to efficiently cope with very large data volumes.

Start Course
1h : 57min
Self-Supervised Learning
Solar energy
Generalization
Transfer learning
Human vs Machine labels
Dwaipayan Chatterjee
•
Prof.in Dr.in Susanne Crewell
•
Dr. Christoph Böhm

AI for cloud classification

Explore the world of cloud dynamics through AI. Uncover structural insights, climate implications, and overcome dataset challenges. Discover neural architecture and optimization. Refine your insights of AI-driven cloud classification for weather prediction and climate modeling.

Start Course
36 min
Interpretability & Analysis
Weather Forecasting
Data Processing
Kaveh Patakchi Yousefi
•
PD Dr. Martin G. Schultz
•

AI for weather modeling

Explore deep learning applications in weather modeling, learn about merging atmospheric model results and observations, and understand how U-Net Convolutional Neural Network (CNN) can be used to improve precipitation predictions.

Start Course
1h : 13min
explainable machine learning
interpretable machine learning
data science
Design patterns
pattern recognition
Ribana Roscher
•
•

Explainable machine learning

Master explainable machine learning with basics and advanced methods. Learn how to interpret linear regression, decision trees, and neural networks. Dive into global and local model-agnostic techniques. Explore Shapley values, occlusion sensitivity, and advanced tools.

Start Course
6 h 18 min

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