Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning

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].

1. Directory Structure

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

2. Usage

Installation

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

Training

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

Evaludation

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.

3.Citation

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~