EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task

Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler

CVPRW 2021 pp. 1132-1142

doi:10.1109/CVPRW53098.2021.00124 /cvprw/2021/requenamesa2021cvprw-earthnet2021/

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 20 m resolution, matching topography and mesoscale (1.28 km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (>×50) 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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Cite

Text

Requena-Mesa et al. "EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00124

Markdown

[Requena-Mesa et al. "EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/requenamesa2021cvprw-earthnet2021/) doi:10.1109/CVPRW53098.2021.00124

BibTeX

@inproceedings{requenamesa2021cvprw-earthnet2021,
  title     = {{EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task}},
  author    = {Requena-Mesa, Christian and Benson, Vitus and Reichstein, Markus and Runge, Jakob and Denzler, Joachim},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {1132-1142},
  doi       = {10.1109/CVPRW53098.2021.00124},
  url       = {https://mlanthology.org/cvprw/2021/requenamesa2021cvprw-earthnet2021/}
}