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Found 7 papers, 4 papers with code
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.
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.
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather.
Ranked #5 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts.
Physics-informed GANs for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society needs better tools for adaptation.
Predicting Landscapes from Environmental Conditions Using Generative Networks
We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies.
