Final Project for University of Michigan EECS 442. Code adapted from Enomoto et al and Python-Clouds.
Downloading and generating the dataset:
# AWS CLI must be installed
cd data/
mkdir images
# Download and unzip the data
aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_3band.tar.gz .
aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_test_AOI_1_Rio_3band.tar.gz .
tar -xvzf SN1_buildings_train_AOI_1_Rio_3band.tar.gz -C images/
tar -xvzf SN1_buildings_test_AOI_1_Rio_3band.tar.gz -C images/
rm SN1_buildings_train_AOI_1_Rio_3band.tar.gz
rm SN1_buildings_test_AOI_1_Rio_3band.tar.gz
# Then split the data into train, validation, and test directories before running the cloud generation script
python3 generate_clouds.py --input_dir train
python3 generate_clouds.py --input_dir validation
python3 generate_clouds.py --input_dir test