  Satellite images are snapshots of the Earth surface. We propose to forecast
them. We frame Earth surface forecasting as the task of predicting satellite
imagery conditioned on future weather. EarthNet2021 is a large dataset suitable
for training deep neural networks on the task. It contains Sentinel 2 satellite
imagery at 20m resolution, matching topography and mesoscale (1.28km)
meteorological variables packaged into 32000 samples. Additionally we frame
EarthNet2021 as a challenge allowing for model intercomparison. Resulting
forecasts will greatly improve (>x50) over the spatial resolution found in
numerical models. This allows localized impacts from extreme weather to be
predicted, thus supporting downstream applications such as crop yield
prediction, forest health assessments or biodiversity monitoring. Find data,
code, and how to participate at www.earthnet.tech
