Official Pytorch Code base for [Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images]

Project

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.

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

Contour and Distance Maps are pre-computed and can be obtained from binary mask and contour. You can use matlab or python codes to obtain them.

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.mat
    |   ├── 002.mat
    |   ├── 003.mat
    └── ├── ...

For test and validation datasets, the same structure as the above.

1. Train the model.

python train.py --train_path ./fields/image --save_path ./model --model_type 'bsinet' --distance_type 'dist_contour' 

2. 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, [email protected] and Mengmeng Li, [email protected].

A GF2 image (1m) is provided for scientific use: https://pan.baidu.com/s/1isg9jD9AlE9EeTqa3Fqrrg, password：bzfd

This code-base uses certain code-blocks and helper functions from Psi-Net

Long J, Li M, Wang X, et al. Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102871.