Semantic Segmentation for Aerial Imagery using Convolutional Neural Network
Extract building and road from aerial imagery
ssai
branch of the above repository$ 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
mass_roads
train: 8458173 patches
valid: 126281 patches
test: 440932 patches
mass_roads_mini, mass_buildings, mass_merged
train: 1119872 patches
valid: 36100 patches
test: 89968 patches
$ python scripts/create_models.py --seed seeds/model_seeds.json --caffe_dir $HOME/lib/caffe/build/install
$ bash shells/train.sh models/Mnih_CNN
will create a directory named results/Mnih_CNN_{started date}
.
$ 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
$ cd lib
$ mkdir build
$ cd build
$ cmake ../
$ make
$ cd results/Mnih_CNN_{started date}
$ python ../../scripts/test_evaluation.py --map_dir ../../data/mass_merged/test/map --result_dir prediction_1000000 --channel 3
$ 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