mitmul / ssai

Semantic Segmentation for Aerial Imagery using Convolutional Neural Network

This repo has been deprecated because whole things are re-implemented by using Chainer and I did refactoring for many codes. So please check this newer version: https://github.com/mitmul/ssai-cnn

Semantic Segmentation for Aerial Imagery

Extract building and road from aerial imagery

Requirements

• OpenCV 2.4.10
• Boost 1.57.0
• Boost.NumPy
• Caffe (modified caffe: https://github.com/mitmul/caffe)
  • NOTE: Build the ssai branch of the above repository

Data preparation

$ bash shells/donwload.sh
$ python scripts/create_dataset.py --dataset multi
$ python scripts/create_dataset.py --dataset single
$ python scripts/create_dataset.py --dataset roads_mini
$ python scripts/create_dataset.py --dataset roads
$ python scripts/create_dataset.py --dataset buildings
$ python scripts/create_dataset.py --dataset merged

Massatusetts Building & Road dataset

• mass_roads

  • train: 8458173 patches

    • epoch: 66079 mini-batches (mini-batch size: 128)

  • valid: 126281 patches

    • epoch: 987 mini-batches (mini-batch size: 128)

  • test: 440932 patches

    • epoch: 3445 mini-batches (mini-batch size: 128)

• mass_roads_mini, mass_buildings, mass_merged

  • train: 1119872 patches

    • epoch: 8749 mini-batches (mini-batch size: 128)

  • valid: 36100 patches

    • epoch: 282 mini-batches (mini-batch size: 128)

  • test: 89968 patches

    • epoch: 703 mini-batches (mini-batch size: 128)

Create Models

$ python scripts/create_models.py --seed seeds/model_seeds.json --caffe_dir $HOME/lib/caffe/build/install

Start training

$ bash shells/train.sh models/Mnih_CNN

will create a directory named results/Mnih_CNN_{started date}.

Prediction

$ cd results/Mnih_CNN_{started date}
$ python ../../scripts/test_prediction.py --model predict.prototxt --weight snapshots/Mnih_CNN_iter_1000000.caffemodel --img_dir ../../data/mass_merged/test/sat --channel 3

Build Library for Evaluation

$ cd lib
$ mkdir build
$ cd build
$ cmake ../
$ make

Evaluation

$ cd results/Mnih_CNN_{started date}
$ python ../../scripts/test_evaluation.py --map_dir ../../data/mass_merged/test/map --result_dir prediction_1000000 --channel 3

Model averaging

$ python ../scripts/batch_evaluation.py --offset True
$ mkdir Mnih_CNN_Merged
$ cd Mnih_CNN_Merged
$ python ../../scripts/test_evaluation.py --map_dir ../../data/mass_merged/test/map --result_dir ./prediction_100000 --channel 3 --offset 0 --pad 31