Machine learning tutorial for remote sensing scientists. We follow the lifecycle of a machine learning project with the example of predicting wind speed from CyGNSS data.
Developed for the D4G Remote Sensing Workshop, GFZ, Potsdam, 13.06.2022.
Clone the git repository, install the conda environment provided in environment.yml and follow the steps described in tutorial/D4G-Walkthrough.ipynb
You can run the tutorial in Google Colab (colab.reseach.google.com) using tutorial/D4G-Interactive.ipynb.
Contact: arnold@dkrz.de, consultant-helmholtz.ai@dkrz.de
Author: Caroline Arnold, German Climate Computing Centre DKRZ / Helmholtz AI, 2022
Acknowledgement:
CYGNSS. 2020. CYGNSS Level 2 Science Data Record Version 3.0. Ver. 3.0. PO.DAAC, CA, USA. Dataset accessed at https://doi.org/10.5067/CYGNS-L2X30.
All instructional material is made available under the [Creative Commons Attribution license][cc-by-human]. The following is a human-readable summary of (and not a substitute for) the [full legal text of the CC BY 4.0 license][cc-by-legal].
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