Contains material and instructions necessary to recreate the Machine Learning/AI hands-on exercises prepared for the BETTER project's session at the EO Joint Big Data Hackathon https://www.ec-better.eu/pages/h2020-eo-big-data-hackathon
Land cover changes and inter-annual vegetation performance analysis using ML algorithms
This experiment is hosted in a software repository.
Use git to clone it:
cd /workspace
git clone https://github.com/ec-better/eohackathon-better-ai
cd better_aiThe file env_dmuk_ml.yml contains the Python conda environment for running the notebooks contained in this folder.
From the shell, run:
conda env create --file=env_dmuk_ml.ymlOnce the environment configuration is done, you can activate it:
conda activate env_dmuk_mlOpen the environment.ipynb notebook and update the kernel to use env_dmuk_ml
Run the experiment by executing each of the cells with shift + Enter.
If asked for the credentials, provide your Ellip username and associated Ellip API key.
This experiment is under version control and uses the git flow method (see [https://datasift.github.io/gitflow/IntroducingGitFlow.html])
If not done previously, clone the experiment repository:
git clone https://github.com/ec-better/eohackathon-better-ai
cd better_aiThen, checkout the develop branch with:
git checkout developAt this stage, update the experiment.
When done:
git add -A
git commit -m '<commit message>'
git pullFinally, do a release with:
ciop-releaseThe file env_better_ai.yml contains the Python conda environment for running the notebooks contained in this folder.
From the shell, run:
conda env create --file=env_better_ai.ymlOnce the environment configuration is done, you can activate it:
conda activate better_aiOpen the BetterAI.ipynb notebook and update the kernel to use better_ai
Run the experiment by executing each of the cells with shift + Enter.
Good luck!