This repository includes implementations for binary semantic segmentation, especially for building extraction in satellite images.Link pdf
Furthermore, the boundary enhanced methods (BE module) are also contained in /net/zoo/.

Python >= 3.7.0

Pytorch > =1.9.0

skimage >= 0.18.2

cuda >= 10.1

• The experiments were conducted with cropped images as 512 X 512 size, and splitted 2,912 and 672 for training and test subset, respectively.

• The original dataset can be downloaded Urban3D.

• The data should be arranged like this.

|-- Test
|   `-- Urban3D_Test
|       |-- RGB
|       `-- masks
`-- Train
    `-- Urban3D_Train
        |-- RGB
        `-- masks

• Open /notebook/data_prep.py and make dataframes for train and test set. Urban3D_Train_df.csv and Urban3D_Test_df.csv would be made in /csv/.

• Check and set hyperparameters in /yml/train.yml.
  • Choose model refrered to /net/zoo/__init__py/.
  • Choose area of interest. 6 is default for Urban3D dataset.
  • Set num_stage as following the number of backbone architecture's stage.
  • Set training hyperparameters ; epochs, optimizer, lr, loss functions.
  • If you want to train Boundary Enhancement model, set boundary as True.
• Run /src/train.py.
• result directory and /result/models_weight directory would be created automatically.
• Model weights will be saved in /result/models_weight/{DATASET_NAME}_{MODEL_NAME}_{TRAINING_ID}. TRAINING_ID is an UNIX time when the training was started.

• Check and setup parameters in /yml/infer.yml/.

  • model_name and aoi should be same with those in train.yml.

  • If you want to train Boundary Enhancement model, set boundary as True.

  • Set training_date same as TRAINING_ID.

• Run /src/infer.py.

• Inferred images will be saved in /result/infer/.

• Open /notebook/get_mask_eval.ipynb.
• Check aois and training date. training date is TRAINING_ID in training procedure.
• Running all cells will create mask image from inferred image.
• Evaluation result will show up comparing ground truth and predicted mask. The result will be saved in /result/eval_result/.

• U-Net
• ResUNet++
• TernausNet
• BRR-Net
• USPP
• DE-Net

• DeepGlobe Dataset(Vegas, Paris, Shanghai, Khartoum)
• Urban3D Dataset
• WHU Dataset(aerial and satellite)
• Massachusetts Dataset

|-- data
|   |-- Test
|   `-- Train
|-- nets
|   |-- __init__.py
|   |-- _torch_losses.py
|   |-- assembly_block.py
|   |-- callbacks.py
|   |-- datagen.py
|   |-- infer.py
|   |-- losses.py
|   |-- model_io.py
|   |-- optimizers.py
|   |-- torch_callbacks.py
|   |-- train.py
|   |-- transform.py
|   |-- weights
|   `-- zoo
|-- notebooks
|   |-- __init__.py
|   |-- data_prep.ipynb
|   `-- get_mask_eval.ipynb
|-- result
|   |-- infer
|   |-- infer_masks
|   `-- models_weight
|-- src
|   |-- __init__.py
|   |-- inference.py
|   `-- train.py
|-- utils
|   |-- __init__.py
|   |-- config.py
|   |-- core.py
|   |-- data.py
|   |-- io.py
|   `-- log.py
`-- yml
    |-- infer.yml
    `-- train.yml

This codes are modified and simplified version of Solaris for my own research.