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Found 5 papers, 5 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
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.
Assessing out-of-domain generalization for robust building damage detection
Such models operate in a multi-domain setting: every disaster is inherently different (new geolocation, unique circumstances), and models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event.
