windflow_light
Perform optical flow inference on geostationary satellite images from a pretrained RAFT model.
Install
conda env create -f environment_cpu.yml
Usage
See python predict.py
for a basic example.
Citation
Vandal, T., Duffy, K., McCarty, W., Sewnath, A., & Nemani, R. (2022). Dense feature tracking of atmospheric winds with deep optical flow, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Acknowledgements
External packages and flownet code was used from: https://github.com/celynw/flownet2-pytorch/
Funded by NASA ROSES Earth Science Research from Geostationary Satellite Program (2020-2023)