Awesome

<h2 align="center">Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery</h2>  <h5><a href="http://zhuozheng.top/">Zhuo Zheng</a>, <a href="http://rsidea.whu.edu.cn/">Yanfei Zhong</a>, <a href="https://junjue-wang.github.io/homepage/">Junjue Wang</a> and Ailong Ma</h5> <div align="center"> <img src="https://raw.githubusercontent.com/Z-Zheng/images_repo/master/farseg.png"><br><br> </div>

This is an official implementation of FarSeg in our CVPR 2020 paper Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery.

News

2024/03, source code of FarSeg++ is released.
2023/10, UV6K dataset is publcily available.
2023/07, FarSeg++ is accepted by IEEE TPAMI.

Citation

If you use FarSeg or FarSeg++ in your research, please cite the following paper:

@inproceedings{zheng2020foreground,
  title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4096--4105},
  year={2020}
}
@article{zheng2023farseg++,
  title={FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong and Zhang, Liangpei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  volume={45},
  number={11},
  pages={13715-13729},
  publisher={IEEE}
}

Getting Started

Install SimpleCV

pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git

Requirements:

pytorch >= 1.1.0
python >=3.6

Prepare iSAID Dataset

ln -s </path/to/iSAID> ./isaid_segm

Evaluate Model

1. download pretrained weight in this link

2. move weight file to log directory

mkdir -vp ./log/isaid_segm/farseg50
mv ./farseg50.pth ./log/isaid_segm/farseg50/model-60000.pth

3. inference on iSAID val

bash ./scripts/eval_farseg50.sh

Train Model

bash ./scripts/train_farseg50.sh