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This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are published in this paper.

Requirements

  • Python 3.5 (anaconda with python 3.5.1 is recommended)
    • Chainer 1.5.0.2
    • Cython 0.23.4
    • NumPy 1.10.1
    • tqdm
  • OpenCV 3.0.0
  • lmdb 0.87
  • Boost 1.59.0
  • Boost.NumPy (26aaa5b)

Build Libraries

OpenCV 3.0.0

$ wget https://github.com/Itseez/opencv/archive/3.0.0.zip
$ unzip 3.0.0.zip && rm -rf 3.0.0.zip
$ cd opencv-3.0.0 && mkdir build && cd build
$ bash $SSAI_HOME/shells/build_opencv.sh
$ make -j32 install

If some libraries are missing, do below before compiling 3.0.0.

$ sudo apt-get install -y libopencv-dev libtbb-dev

Boost 1.59. 0

$ wget http://downloads.sourceforge.net/project/boost/boost/1.59.0/boost_1_59_0.tar.bz2
$ tar xvf boost_1_59_0.tar.bz2 && rm -rf boost_1_59_0.tar.bz2
$ cd boost_1_59_0
$ ./bootstrap.sh
$ ./b2 -j32 install cxxflags="-I/home/ubuntu/anaconda3/include/python3.5m"

Boost.NumPy

$ git clone https://github.com/ndarray/Boost.NumPy.git
$ cd Boost.NumPy && mkdir build && cd build
$ cmake -DPYTHON_LIBRARY=$HOME/anaconda3/lib/libpython3.5m.so ../
$ make install

Build utils

$ cd $SSAI_HOME/scripts/utils
$ bash build.sh

Create Dataset

$ bash shells/download.sh
$ bash shells/create_dataset.sh

Dataset     | Training | Validation |  Test

:-------------: | :------: | :--------: | :----: mass_roads | 8580352 | 108416 | 379456 mass_roads_mini | 1060928 | 30976 | 77440 mass_buildings | 1060928 | 30976 | 77440 mass_merged | 1060928 | 30976 | 77440

Start Training

$ CHAINER_TYPE_CHECK=0 CHAINER_SEED=$1 \
nohup python scripts/train.py \
--seed 0 \
--gpu 0 \
--model models/MnihCNN_multi.py \
--train_ortho_db data/mass_merged/lmdb/train_sat \
--train_label_db data/mass_merged/lmdb/train_map \
--valid_ortho_db data/mass_merged/lmdb/valid_sat \
--valid_label_db data/mass_merged/lmdb/valid_map \
--dataset_size 1.0 \
> mnih_multi.log 2>&1 < /dev/null &

Prediction

python scripts/predict.py \
--model results/MnihCNN_multi_2016-02-03_03-34-58/MnihCNN_multi.py \
--param results/MnihCNN_multi_2016-02-03_03-34-58/epoch-400.model \
--test_sat_dir data/mass_merged/test/sat \
--channels 3 \
--offset 8 \
--gpu 0 &

Evaluation

$ PYTHONPATH=".":$PYTHONPATH python scripts/evaluate.py \
--map_dir data/mass_merged/test/map \
--result_dir results/MnihCNN_multi_2016-02-03_03-34-58/ma_prediction_400 \
--channel 3 \
--offset 8 \
--relax 3 \
--steps 1024

Results

Conventional methods

Model Mass. Buildings Mass. Roads Mass.Roads-Mini
MnihCNN 0.9150 0.8873 N/A
MnihCNN + CRF 0.9211 0.8904 N/A
MnihCNN + Post-processing net 0.9203 0.9006 N/A
Single-channel 0.9503062 0.91730195 (epoch 120) 0.89989258
Single-channel with MA 0.953766 0.91903522 (epoch 120) 0.902895

Multi-channel models (epoch = 400, step = 1024)

Model Building-channel Road-channel Road-channel (fixed)
Multi-channel 0.94346856 0.89379946 0.9033020025
Multi-channel with MA 0.95231262 0.89971473 0.90982972
Multi-channel with CIS 0.94417078 0.89415726 0.9039476538
Multi-channel with CIS + MA 0.95280431 0.90071099 0.91108087

Test on urban areas (epoch = 400, step = 1024)

Model Building-channel Road-channel
Single-channel with MA 0.962133 0.944748
Multi-channel with MA 0.962797 0.947224
Multi-channel with CIS + MA 0.964499 0.950465

x0_sigma for inverting feature maps

159.348674296

After prediction for single MA

$ bash shells/predict.sh
$ python scripts/integrate.py --result_dir results --epoch 200 --size 7,60
$ PYTHONPATH=".":$PYTHONPATH python scripts/evaluate.py --map_dir data/mass_merged/test/map --result_dir results/integrated_200 --channel 3 --offset 8 --relax 3 --steps 256
$ PYTHONPATH="." python scripts/eval_urban.py --result_dir results/integrated_200 --test_map_dir data/mass_merged/test/map --steps 256

Pre-trained models and Predicted results

Reference

If you use this code for your project, please cite this journal paper:

Shunta Saito, Takayoshi Yamashita, Yoshimitsu Aoki, "Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks", Journal of Imaging Science and Technology, Vol. 60, No. 1, pp. 10402-1-10402-9, 2015