BsiNet
Official Pytorch Code base for "Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images"
Introduction
This paper presents a new multi-task neural network BsiNet to delineate agricultural fields from remote sensing images. BsiNet learns three tasks, i.e., a core task for agricultural field identification and two auxiliary tasks for field boundary prediction and distance estimation, corresponding to mask, boundary, and distance tasks, respectively.
Using the code:
The code is stable while using Python 3.7.0, CUDA >=11.0
- Clone this repository:
git clone https://github.com/long123524/BsiNet-torch
cd BsiNet-torch
To install all the dependencies using conda or pip:
PyTorch
TensorboardX
OpenCV
numpy
tqdm
Preprocessing
Using the code preprocess.py to obtain contour and distance maps.
Data Format
Make sure to put the files as the following structure:
inputs
└── <train>
├── image
| ├── 001.tif
│ ├── 002.tif
│ ├── 003.tif
│ ├── ...
|
└── mask
| ├── 001.tif
| ├── 002.tif
| ├── 003.tif
| ├── ...
└── contour
| ├── 001.tif
| ├── 002.tif
| ├── 003.tif
| ├── ...
└── dist_contour
| ├── 001.tif
| ├── 002.tif
| ├── 003.tif
└── ├── ...
For test and validation datasets, the same structure as the above.
Training and testing
- Train the model.
python train.py --train_path ./fields/image --save_path ./model --model_type 'bsinet' --distance_type 'dist_contour'
- Evaluate.
python test.py --model_file ./model/150.pt --save_path ./save --model_type 'bsinet' --distance_type 'dist_contour' --val_path ./test_image
If you have any questions, you can contact us: Jiang long, hnzzyxlj@163.com and Mengmeng Li, mli@fzu.edu.cn.
GF dataset
A GF2 image (1m) is provided for scientific use: https://pan.baidu.com/s/1isg9jD9AlE9EeTqa3Fqrrg, password:bzfd Google drive:https://drive.google.com/file/d/1JZtRSxX5PaT3JCzvCLq2Jrt0CBXqZj7c/view?usp=drive_link
A pretrained weight
A pretrained weight on a Xinjiang GF-2 image is provided: https://pan.baidu.com/s/1PJBcr4jJR_znqhs-tdEpug password:f2pg Google drive: https://drive.google.com/file/d/12WOseQqBNQQZGe5xSGgEvUZmOd5kKd5Z/view?usp=sharing
Acknowledgements:
This code-base uses certain code-blocks and helper functions from Psi-Net
Citation:
Long J, Li M, Wang X, et al. Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102871.