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Found 4 papers, 3 papers with code
SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods.
Multi-modal learning for geospatial vegetation forecasting
Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe.
Deep Learning for Global Wildfire Forecasting
We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Forecasting the state of vegetation in response to climate and weather events is a major challenge.
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