Overview of scripts:

BIA_map_vis_and_analyze.py - visualizes map and analyzes continent-wide mapping (e.g., Figures 1, 2, and S8, S9, S11, S12, S13)

BIA_map_vis_radar.py - visualizes uncertainties of BIA predictions related to the inclusion of radar data (Figure S7)

BIA_map_vis_uncertainties.py - visualizes uncertainties of BIA predictions (Figure 3)

check_checkpoint.py - checks training results (e.g., learning curve)

check_num_params.py - checks number of parameters of model

continentwide_predictions.py - generates continent-wide predictions, plots Figure S5

DataPreparation.py - generates data tiles for training, validation, testing, and for continent-wide predictions

dataset.py - reads in data in dataloader and applies different data augmentations

download_data.py - function to download data from MODIS (based on https://www.moonbooks.org/Articles/How-to-download-a-file-from-NASA-LAADS-DAAC-using-python-/)

explore_handlabels_MOAgrainsize.py - compares handlabels to MOA grainsize data (Figure S10)

merge_daily_passes.py - merges daily composites of MODIS into a multi-day composite

merge_ensemble_predictions.py - merges 40 BIA maps into a single BIA map

model.py - defines CNN model, script based on U-Net implementaion on https://github.com/aladdinpersson/Machine-Learning-Collection

parameter_settings_final_model_1.json - example of parameter settings file to train the CNN

perform_rand_search.py - runs random search for hyper parameter optimizing and compares results

Performance_ValTestTiles.py - estimates performance in validation and test squares

plot_MODISnewcomposite.py - generates Figure S1 to visualize new MODIS composite

plot_overview_data.py - plots overview of input data (Figure S6)

plot_overview_tiling.py - plots overview of datasplit (training, validation, and testing; Figure S2)

python_wrapper_functions.py - defines different functions to reproject and process MODIS data

python_wrapper_main.py - python wrapper to download and reproject MODIS data automatically

QA_tomask.py - processes quality bands of MODIS to mask out e.g., cloudy observations

train.py - trains CNN, script based on U-Net implementaion on https://github.com/aladdinpersson/Machine-Learning-Collection utils.py - different functions used for training CNN

vis_existing_vs_new.py - visualizes existing labels vs BIA outlines generated in this study and handlabels (Figures S3 and S4)