Broad Area Satellite Imagery Semantic Segmentation
==============================================================================
Code Overview
This package segments satellite imagery over large swaths of land (or sea). The included examples use SpaceNet labels to identify roads in high resolution satellite imagery. Access to a GPU is required for training, though inference will function (slowly) on a CPU. See our blog post for further details.
==============================================================================
-
Installation
A. Install nvidia-docker B. Build container nvidia-docker build -t basiss path_to_basiss/docker C. Download scripts download this github repo (or put it in the docker file...) D. Run container nvidia-docker run -it -v /raid:/raid --name bassiss_train basiss
==============================================================================
- Download SpaceNet data, see the instructions.
==============================================================================
-
Create training masks. The script used here is a lightly modified version of the code described in our blog. Execute these scripts in a unique conda environment; conda install insctuctions are here. The commands below create the training images; replace "train" with "test" to create testing images.
basiss_path=/raid/local/src/basiss cd $basiss_path/src conda env create -f apls_environment.yml # to deactivate environment: source deactivate source activate apls_environment
#for details on arguments type: python create_spacenet_masks.py --help python $basiss_path/create_spacenet_masks.py
--path_data=/path_to_spacenet_data/AOI_2_Vegas_Train
--output_df_path=$basiss_path/packaged_data/AOI_2_Train_2m_file_locs.csv
--buffer_meters=2
--n_bands=3
--make_plots=0
--overwrite_ims=0
==============================================================================
-
Train a model
# train Las Vegas SpaceNet 3-band data with unet, and sliced into 400 # pixel cutouts basiss_path=/raid/local/src/basiss outname=AOI_2_Vegas_unet_2m_train cd $basiss_path nohup python -u src/basiss.py \ --path $basiss_path \ --model unet \ --mode train \ --file_list AOI_2_Train_2m_file_locs.csv \ --slice_x 400 --slice_y 400 \ --stride_x 300 --stride_y 300 \ --n_bands 3 \ --n_classes 2 \ --batchsize 32 \ --validation_split 0.1 \ --early_stopping_patience 4 \ --epochs 128 \ --gpu 0 \ --prefix $outname > \ results/$outname.log & tail -f results/$outname.log
==============================================================================
-
Test on images of arbitrary size
# test Las Vegas SpaceNet 3-band data with unet, and sliced into 400 # pixel cutouts basiss_path=/raid/local/src/basiss outname=AOI_2_Vegas_unet_2m_test cd $basiss_path nohup python -u src/basiss.py \ --path $basiss_path \ --model unet \ --mode test \ --file_list massive_file_list.csv \ --model_weights AOI_2_Vegas_unet_2m_train_model_best.hdf5 \ --slice_x 400 --slice_y 400 \ --stride_x 300 --stride_y 300 \ --n_bands 3 \ --n_classes 2 \ --batchsize 16 \ --gpu 3 \ --prefix $outname > \ results/$outname.log & tail -f results/$outname.log
