Leipzig University (DE)
DEEP EXTREMES is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster.
The AI4SCIENCE ITT had 2 main objectives:
The DEEP EXTREMES project has a focus on compound heat and drought events at global scale, looking at detection based on long-term climate and land-surface data, combining EO archives and other observation data, with methods tailored to multivariate event detection.
The principle is to start from sampling a subset of large events in Sentinel era and zooming into the events and in unaffected areas around the event with high-dimensional “mini cubes”.
The activity then aims to train complementary deep-learning methods for prediction and understanding dynamics in such events, implement the tested and validated workflow in a cloud environment and developing it further based on community feedback. Science community engagement is planned via workshops and science discussions to further develop the proposed framework.
Additional information and resources can be found at the project website.
DeepExtremes/cv-groups-minicubes: v1.0.0
Collection of Julia scripts to download DeepExtremes minicubes registry and split the dataset into cross-validation folds based on location. Cite as: Mélanie, Weynants, and Fabian Gans. ‘Deepextremes/cv-groups-minicubes: V1.0.0’. Zenodo, 21 December 2023
Dheed: a global database of dry and hot extreme events
Biodiversity and Climate Extremes: Known Interactions and Research Gaps
Earth's Future (2024)
Biodiversity and Climate Extremes: Known Interactions and Research Gaps
Earth's Future (2024)
ARXIV Computer Vision and Pattern Recognition (2023)