Summary of Explainable Earth Surface Forecasting Under Extreme Events, by Oscar J. Pellicer-valero et al.
Explainable Earth Surface Forecasting under Extreme Events
by Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji, Miguel D. Mahecha, Gustau Camps-Valls
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to forecasting and understanding the impacts of extreme climate events on ecosystems using high-dimensional Earth observation data. A convolutional long short-term memory-based architecture is trained on the DeepExtremeCubes dataset, which includes labeled extreme events, meteorological data, vegetation land cover, and topography maps from locations affected by extreme climate events. The model achieves an R² score of 0.9055 in predicting future reflectances and vegetation impacts through kernel normalized difference vegetation index. Explainable artificial intelligence is used to analyze the model’s predictions during a Central South America compound heatwave and drought event, revealing that minimum anomalies of evaporation and surface latent heat flux are key predictors under extreme conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how climate change affects our planet by using special computer models to analyze big amounts of Earth observation data. The model is trained on a dataset that includes information about extreme weather events, like floods and droughts, as well as other important factors like temperature, wind direction, and soil moisture. By analyzing this data, the model can predict how certain areas will be affected by future extreme weather events. This can help us prepare for these events and protect our ecosystems. |
Keywords
* Artificial intelligence * Temperature
