Description

The Artificial Intelligence for Disentangling Extremes (AIDE) toolbox allows for tackling generic problems of detection and impact assessment of events such as tropical cyclones and severe convective storms, heat waves, and droughts, as well as persistent winter extremes, among others. The open-source toolbox integrates advanced ML models, ranging in complexity, assumptions, and sophistication, and can yield spatio-temporal explicit output maps with probabilistic heatmap estimates. We included supervised and unsupervised algorithms, deterministic and probabilistic, convolutional and recurrent neural networks, and detection methods based on density estimation. The toolbox is intended for scientists, engineers, and students with basic knowledge of extreme events detection, outlier detection techniques, and Deep Learning (DL), as well as Python programming with basic packages (Numpy, Scikit-learn, Matplotlib) and DL packages (PyTorch, PyTorch Lightning).

Potential User Groups: Researchers in applied packages of XAIDA or otherwise concerned with extreme event detection, impact assessment, as well as model understanding.

Usage Guide

A journal paper has been published in IEEE Geosciences and Remote Sensing Magazine (GRSM) (https://doi.org/10.1109/MGRS.2024.3382544). Moreover, we provide a ReadTheDocs site with tutorials (https://aidextremes.readthedocs.io/en/latest/). Last but not least, the AIDE toolbox was introduced during a XAIDA Webinar last April, 2024 (https://www.youtube.com/watch?v=P6bO3rv7LcQ&t=1281s).

Availability: The toolbox is publicly available on the GitHub platform (https://github.com/IPL-UV/AIDE/). 

Use Cases

• Drought detection task in Russia (Gonzalez-Calabuig, M., Cortés-Andrés, J., Williams, T. K. E., Zhang, M., Pellicer-Valero, O. J., Fernández-Torres, M. Á., & Camps-Valls, G. (2024). The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine, 12(2), 113-118.)

References

• Gonzalez-Calabuig, M., Cortés-Andrés, J., Williams, T. K. E., Zhang, M., Pellicer-Valero, O. J., Fernández-Torres, M. Á., & Camps-Valls, G. (2024). The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine, 12(2), 113-118.