Content Comparison

library(ForestForesight)# this will load the FF library and the variables in the config.yml file
# Make sure you have DATA_FOLDER in config.yml in your working directoryrun ff_environment previously, 
# if not you have to set the parameter download_folder yourself
download_folder <- Sys.getenv("DATAFF_FOLDER")

print(download_folder)
#if the previous line returns empty it means your system environment variables were not set. You can then create a folder
#somewhere in your file explorer and use  the following line to select that folder:
download_folder <- choose.dir() 

# Choose an identifier (country code, tile ID, or SpatVector). 
# ONLY RUN 1 OF THE FOLLOWING TWO LINES
identifier <- "LAOPER"  # Example: LaosPeru
identifier <- vect(file.choose()) # Make sure this is a valid SpatVector object.  

# Part 1: Download only predictions
ff_sync(
    ff_folder = download_folder,
    identifier = identifier,
    download_data = FALSE,          # Don't download input data
    download_groundtruth = FALSE,   # Don't download ground truth
    download_predictions = TRUE,     # Only download predictions
    date_start_date = "2023-01-01"       # Only download data since 2023
)
# check the folder in your file explorer to see that in your download_folder there is now a download folder called predictions
# which contains tif files for predictions for every month since the start date for the selected country or the countries
# within the selected area

# Part 2: Download complete dataset
ff_sync(
    ff_folder = download_folder,
    identifier = identifier,
    features = "Modelsmall model",             # Download model-related features
    download_data = TRUE,          # Download input data
    download_groundtruth = TRUE,   # Download ground truth
    download_predictions = FALSE,   # Download predictions
    download_model = TRUE,         # Download the model
    date_start_date = "2023-01-01"       # Only download data since 2023
)
# check that now also a folder called preprocessed and a folder called models has been created. These contain
# groundtruth data, preprocessed model input data and a pretrained model.

# check all the other available options of ff_sync
?ff_sync