The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]
We introduce the Artificial Intelligence for Disentangling Extremes (AIDE) toolbox that allows for anomaly detection, extreme event analysis, and impact assessment in remote sensing and geoscience applications. AIDE integrates advanced machine learning (ML) models, ranging in complexity, assumptions...
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| Veröffentlicht in: | IEEE geoscience and remote sensing magazine 2024-06, Vol.12 (2), p.113-118 |
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| container_end_page | 118 |
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| container_title | IEEE geoscience and remote sensing magazine |
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| creator | Gonzalez-Calabuig, Maria Cortes-Andres, Jordi Williams, Tristan Keith Ellis Zhang, Mengxue Pellicer-Valero, Oscar Jose Fernandez-Torres, Miguel-Angel Camps-Valls, Gustau |
| description | We introduce the Artificial Intelligence for Disentangling Extremes (AIDE) toolbox that allows for anomaly detection, extreme event analysis, and impact assessment in remote sensing and geoscience applications. AIDE integrates advanced machine learning (ML) models, ranging in complexity, assumptions, and sophistication, and can yield spatiotemporal explicit monitoring maps with probabilistic estimates. Supervised and unsupervised algorithms, deterministic and probabilistic, convolutional and recurrent neural networks (CNNs and RNNs), as well as methods based on density estimation, are covered by this framework. The open source toolbox is intended for scientists, engineers, and students with basic knowledge of remote sensing and geosciences working in anomaly detection, deep learning (DL), and explainable AI (XAI), and is available at https://github.com/IPL-UV/AIDE . |
| doi_str_mv | 10.1109/MGRS.2024.3382544 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Analytical models Anomaly detection Artificial intelligence Data models Pipelines Probabilistic logic Recurrent neural networks Solid modeling Time series analysis |
| title | The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets] |
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