This repository is part of the EVOLAND Horizon Europe project. It provides a User Defined Function to extract Sentinel-2 embeddings with prosailVAE model. The embeddings present mean and log-variance of 11 bio-physical variables (22 features in total).
Y. Zérah, S. Valero and J. Inglada, “Physics-Driven Probabilistic Deep Learning for the Inversion of Physical Models With Application to Phenological Parameter Retrieval From Satellite Times Series,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-23, 2023, Art no. 4404723, doi: 10.1109/TGRS.2023.3284992.
The respository include the User Defined Function implemented using a ONNX export of the best model, as well as a runtime script allowing to use it with your OpenEO account.
$ pip install -e git+https://github.com/ekalinicheva/openeo_pvae.git$ run_openeo_pvae --start_date 2020-07-05 --end_date 2020-07-30 --extent 5.1 5.25 51 51.1 --output results/Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.
This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.