Awesome

WiCoNet

Pytorch codes of 'Looking Outside the Window: Wider Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images' [paper]

BLU dataset [download link] [Baidu Netdisk]

To be updated:

• Codes for the BLU dataset
• Codes for the GID
• Codes for the Potsdam dataset
• Optimizing the codes to easily switch datasets

How to Use

1. Split the data into training, validation and test set and organize them as follows:

YOUR_DATA_DIR

• Train
  • image
  • label
• Val
  • image
  • label
• Test
  • image
  • label

2. Change the training parameters in Train_WiCo_BLU.py, especially the data directory.

3. To evaluate, change also the parameters in Eval_WiCo_BLU.py, especially the data directory and the checkpoint path.

If you find our work useful or interesting, please consider to cite:

L. Ding et al., "Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3168697.