Intercomparison Accuracy Assessment

Prerequisite: Adding Labeled Data

Instructions

1. Setup

Inside a local crop-mask directory run the following commands to setup your project for intercomparison:

conda activate landcover-mapping         # Activates python environment
gcloud auth application-default login    # Logs into Google Cloud (where data is stored with dvc)
earthengine authenticate                 # Logs into earthengine
git checkout master                      # Switches to the master branch
git pull                                 # Pulls the latest code (including .dvc versioning files)
dvc pull                                 # Pulls the latest data (using .dvc versioning files)
ROI="<your ROI name>"
git checkout -b$ROI'-intercomparison'    # Creates new branch where intercomparison will be done
mkdir -p maps/$ROI                       # Creates folder for ROI  
cp -r maps/_templates/. maps/$ROI        # Copies over template notebooks

2. Add ROI to src/compare_covermaps.py

Specifically update the python dictionary TEST_CODE and TEST_COUNTRIES

3. Start and run intercomparison notebook

jupyter notebook .

Navigate to the notebook in maps/$ROI/intercomparison.ipynb

Update the description and cell 3 and run all cells. See example below:

4. Push new intercomparison notebook to Github

git add .
git commit -m'Created new dataset'
git push