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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 directory
download_folder <- Sys.getenv("DATA_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 <- dir.choose()
# Choose an identifier (country code, tile ID, or SpatVector).
# ONLY RUN 1 OF THE FOLLOWING TWO LINES
identifier <- "LAO" # Example: Laos
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 = "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 = "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 = "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 |
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