Broad Area Satellite Imagery Semantic Segmentation

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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.

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1. 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		
   

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2. Download SpaceNet data, see the instructions.

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3. 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

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4. 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
   

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5. 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