Open source code for the detection, characterization and impact assessment of spatio-temporal extreme events
The AIDE toolbox consists of a full pipeline for the detection, characterization and impact assessment of extreme events using ML and computer vision tools. Its purpose is to provide an ML-based generic and flexible pipeline to detect, characterize and evaluate the impacts of extreme events based on spatio-temporal Earth and climate observational data. The pipeline consists of three different stages:
- Data loading and pre-processing
- ML architecture selection and training
- Evaluation and visualization of results
# 1) Create an empty pip environment
python3 -m venv ./aide_env
# 2) Activate the environment
source ./aide_env/bin/activate
# 3) Install dependencies
pip install -r requirements.txt install libs
# 4) Run main.py of AIDE using a config file. Some examples:
# DroughtED database and K-Nearest Neighbors (KNN) model (from PyOD)
python main.py --config=./configs/config_DroughtED_OutlierDetection.yaml
# DroughtED database and LSTM-based architecture (user-defined)
python main.py --config=./configs/config_DroughtED_DeepLearning.yamlDocumentation can be found on Read the Docs, as well as in the docs/ on the toolbox source.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
If you use this code for your research, please cite The AIDE Toolbox: AI for Disentangling Extreme Events:
Gonzalez-Calabuig, M., Cortés-Andrés, J., Williams, T., Zhang, M., Pellicer-Valero, O.J., Fernández-Torres, M.Á., Camps-Valls, G.: The AIDE Toolbox: AI for Disentangling Extreme Events. IEEE Geoscience and Remote Sensing Magazine 12(3), 1–8 (2024). https://doi.org/10.1109/MGRS.2024.3382544
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 101003469.
