In CVPR 2019 [pdf] [supplementary]
- Convert Spacenet 11-bit images to 8-bit Images, country wise.
- Create Gaussian Road Masks, country wise.
- Move all data to single folder.
Default Spacenet3 tree structure assumed.
spacenet3
│
└───AOI_2_Vegas_Train
│ └───RGB-PanSharpen
│ └───geojson
│ └───summaryData
│
└───AOI_3_Paris_Train
│ └───RGB-PanSharpen
│ └───geojson
│ └───summaryData
|
└───AOI_4_Shanghai_Train
| .
|
└───AOI_5_Khartoum_Train
| .
|
cd preprocessing
bash prepare_spacenet.sh /spacenet3
Spacenet tree structure created by preprocessing.
spacenet3
|
└───full
│ └───gt
│ └───images
Download DeepGlobe Road dataset in the following tree structure.
deepglobe
│
└───train
│ └───gt
│ └───images
Script to split and save in '/data/spacenet' and '/data/deepglobe'.
bash split_data.sh /spacenet3/full /data/spacenet/ .png .png
bash split_data.sh /deepglobe/train /data/deepglobe _sat.jpg _mask.png
data/spacenet
| train.txt
| val.txt
| train_crops.txt # created by script
| val_crops.txt # created by script
|
└───train
│ └───gt
│ └───images
└───val
│ └───gt
│ └───images
└───train_crops # created by script
│ └───gt
│ └───images
└───val_crops # created by script
│ └───gt
│ └───images
python create_crops.py --base_dir /data/spacenet/ --crop_size 650 --crop_overlap 215 --im_suffix .png --gt_suffix .png
python create_crops.py --base_dir /data/deepglobe/ --crop_size 512 --crop_overlap 256 --im_suffix _sat.jpg --gt_suffix _mask.png
- Road Orientation - Notebook
- Training Dataset - Notebook
- Linear Corruption (Connectivity Refinement) - Notebook
Train Multi-Task learning framework to predict road segmentation and road orientation.
Training MTL Help
usage: train_mtl.py [-h] --config CONFIG
--model_name {LinkNet34MTL,StackHourglassNetMTL}
--dataset {deepglobe,spacenet}
--exp EXP
[--resume RESUME]
[--model_kwargs MODEL_KWARGS]
[--multi_scale_pred MULTI_SCALE_PRED]
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file path
--model_name {LinkNet34MTL,StackHourglassNetMTL}
Name of Model = ['StackHourglassNetMTL',
'LinkNet34MTL']
--exp EXP Experiment Name/Directory
--resume RESUME path to latest checkpoint (default: None)
--dataset {deepglobe,spacenet}
select dataset name from ['deepglobe', 'spacenet'].
(default: Spacenet)
--model_kwargs MODEL_KWARGS
parameters for the model
--multi_scale_pred MULTI_SCALE_PRED
perform multi-scale prediction (default: True)
Sample Usage
- Training with StackModule
CUDA_VISIBLE_DEVICES=0,1 python train_mtl.py --config config.json --dataset deepglobe --model_name "StackHourglassNetMTL" --exp dg_stak_mtl
- Training with LinkNet34
CUDA_VISIBLE_DEVICES=0,1 python train_mtl.py --config config.json --dataset deepglobe --model_name "LinkNet34MTL" --exp dg_L34_mtl --multi_scale_pred false
- Please use Java implementation to compute APLS provided by Spacenet Challenge. - Visualizer tool
- For more info refer issue #13
- Training with Linear Artifacts/Corruption (using LinkNe34 Architecture)
CUDA_VISIBLE_DEVICES=0,1 python train_refine_pre.py --config config.json --dataset spacenet --model_name "LinkNet34" --exp spacenet_L34_pre_train_with_corruption --multi_scale_pred false
If you find our work useful in your research, please cite:
@InProceedings{Batra_2019_CVPR,
author = {Batra, Anil and Singh, Suriya and Pang, Guan and Basu, Saikat and Jawahar, C.V. and Paluri, Manohar},
title = {Improved Road Connectivity by Joint Learning of Orientation and Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
- Dataset for Connectivity Refinement
- Training file for Road connectivity refinement
- Dataset for Junction Learning