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Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks including an unofficial implementation of Volodymyr Mnih's methods
| Date | Stars |
|---|---|
| 2026-07-31 | 262 |
| 2026-08-05 | 262 |
| 2026-08-06 | 262 |
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This is an implementation of [Volodymyr Mnih's dissertation](https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf) methods on his [Massachusetts road & building dataset](https://www.cs.toronto.edu/~vmnih/data/) and my original methods that are published in [this paper](http://www.ingentaconnect.com/content/ist/jist/2016/00000060/00000001/art00003). # 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](https://github.com/ndarray/Boost.NumPy/tree/26aaa5b62e6170f2ccde179b46f1a49c4011fc9d)) # 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 CI
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matched fp:072b45f82c229433, llm:Repository description: "Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks including an unofficial implementation of Volodymyr Mnih's methods" (language: Python).
matched fp:072b45f82c229433, llm:Repository description: "Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks including an unofficial implementation of Volodymyr Mnih's methods" (language: Python).