EarthNet2021: A large-scale dataset and challenge for Earth surface
forecasting as a guided video prediction task
- URL: http://arxiv.org/abs/2104.10066v1
- Date: Fri, 16 Apr 2021 09:47:30 GMT
- Title: EarthNet2021: A large-scale dataset and challenge for Earth surface
forecasting as a guided video prediction task
- Authors: Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge,
Joachim Denzler
- Abstract summary: 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.
Resulting forecasts will greatly improve over the spatial resolution found in numerical models.
- Score: 12.795776149170978
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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
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