This is an official PyTorch implementation of a semi-supervised learning framework for flood mapping. The manuscript can be visited via https://ieeexplore.ieee.org/abstract/document/9924583/. The Calgary-Flood datasets used in this paper can be accessed from [GoogleDirve] or [BaiduDisk].

After obtain the Calgary-Flood datasets, you need to process first and generate lists of image/label files and place as the structure shown below. Every txt file contains the full absolute path of the files, each image/label per line. Note: for train_unsup_image.txt, you can just copy test_image.txt and then rename it to train_unsup_image.txt.

/root
    /train_image.txt
    /train_label.txt
    /test_image.txt
    /test_label.txt
    /val_image.txt
    /val_label.txt
    /train_unsup_image.txt

The code is developed using Python 3.8 with PyTorch 1.9.0. The code is developed and tested using singel RTX 2080 Ti GPU.

(1) Clone this repo.

git clone https://github.com/YJ-He/Flood_Mapping_SSL.git

(2) Create a conda environment.

conda env create -f environment.yaml
conda activate flood_mapping

1. set root_dir and hyper-parameters configuration in ./configs/config.cfg.
2. run python train.py.

1. set root_dir and hyper-parameters configuration in ./configs/config.cfg.
2. set pathCkpt in test.py to indicate the model checkpoint file.
3. run python test.py.

If this repo is useful in your research, please kindly consider citing our paper as follow.

@article{he2022enhancement,
  title={Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semi-Supervised Learning},
  author={He, Yongjun and Wang, Jinfei and Zhang, Ying and Liao, Chunhua},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2022},
  publisher={IEEE}
}

If our work give you some insights and hints, star me please! Thank you~