| # STTNet | |
| Paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers | |
| 1. Prepare Data | |
| Prepare data for training, validation, and test phase. All images are with the resolution of $512 \times 512$. Please refer to the directory of **Data**. | |
| For larger images, you can patch the images with labels using **Tools/CutImgSegWithLabel.py**. | |
| 2. Get Data List | |
| Please refer to **Tools/GetTrainValTestCSV.py** to get the train, val, and test csv files. | |
| 3. Get Imgs Infos | |
| Please refer to **Tools/GetImgMeanStd.py** to get the mean value and standard deviation of the all image pixels in training set. | |
| 4. Modify Model Infos | |
| Please modify the model information if you want, or keep the default configuration. | |
| 5. Run to Train | |
| Train the model in **Main.py**. | |
| 6. [Optional] Run to Test | |
| Test the model with checkpoint in **Test.py**. | |
| We have provided pretrained models on INRIA and WHU Datasets. The pt models are in folder **Pretrain**. | |
| If you have any questions, please refer to [our paper](https://www.mdpi.com/2072-4292/13/21/4441) or contact with us by email. | |
| ``` | |
| @Article{rs13214441, | |
| AUTHOR = {Chen, Keyan and Zou, Zhengxia and Shi, Zhenwei}, | |
| TITLE = {Building Extraction from Remote Sensing Images with Sparse Token Transformers}, | |
| JOURNAL = {Remote Sensing}, | |
| VOLUME = {13}, | |
| YEAR = {2021}, | |
| NUMBER = {21}, | |
| ARTICLE-NUMBER = {4441}, | |
| URL = {https://www.mdpi.com/2072-4292/13/21/4441}, | |
| ISSN = {2072-4292}, | |
| DOI = {10.3390/rs13214441} | |
| } | |
| ``` | |