Official Pytorch Code base for "Integrating Segment Anything Model derived boundary prior and high-level semantics for cropland extraction from high-resolution remote sensing images"
We propose a two-flow network based on multitask VFM, named TFNet, to extract croplands with well-delineated boundaries from high-resolution remote sensing images. TFNet consists of a mask flow and a boundary flow. It first uses a VFM as visual encoder to obtain universal semantic features regarding croplands, and then aggregates them into the two flows.
The code is stable while using Python 3.9.0, CUDA >=11.0
- Clone this repository:
git clone https://github.com/long123524/TFNet
cd TFNetInstall FastSAM following the instructions. Modify the Ultralytics source files following the instructions at: 'TFNet/models/FastSAM/README.md'.
Using the code preprocess.py to obtain boundary maps.
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
| ├── ...
└── ...
For testing and validation datasets, the same structure as the above.
python train.py
python pred.py
python eval.py
A pretrained weight of FastSAM is provided: https://drive.google.com/file/d/1uzeVfA4gEQ772vzLntnkqvWePSw84F6y/view?usp=sharing.
Shandong GF-2 image:https://drive.google.com/file/d/1JZtRSxX5PaT3JCzvCLq2Jrt0CBXqZj7c/view?usp=drive_link A corresponding partial cropland label can be accessible at https://drive.google.com/file/d/19OrVPkb0MkoaUvaax_9uvnJgSr_dcSSW/view?usp=sharing.
If you find this work useful or interesting, please consider citing the following references.
Citation 1:
{Authors: Long jiang, Zhao hang, Li Mengmeng, et al;
Institute: The Academy of Digital China (Fujian), Fuzhou University; Chinese Academy of Sciences
Article Title: Integrating Segment Anything Model derived boundary prior and high-level semantics for cropland extraction from high-resolution remote sensing images,
Publication: IEEE Geoscience and Remote Sensing Letters,
Year: 2024,
Volume:21,
Page: 1-5,
DOI: 10.1109/LGRS.2024.3454263
}
Citation 2:
{Authors: Long Jiang, Li Mengmeng, Wang Xiaoqin, et al;
Institute: The Academy of Digital China (Fujian), Fuzhou University,
Article Title: Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images,
Publication: International Journal of Applied Earth Observation and Geoinformation,
Year: 2022,
Volume:112
Page: 102871,
DOI: 10.1016/j.jag.2022.102871
}
Citation 3:
{Authors: Li Mengmeng, Long Jiang, et al;
Institute: The Academy of Digital China (Fujian), Fuzhou University,
Article Title: Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images,
Publication: ISPRS Journal of Photogrammetry and Remote Sensing,
Year: 2023,
Volume:200
Page: 24-40,
DOI: 10.1016/j.isprsjprs.2023年04月01日9
}
Will be accessible at https://github.com/NanNanmei/HBGNet, more details can be found at a recent collaborative paper "A large-scale VHR parcel dataset and a novel hierarchical semantic boundary-guided network for agricultural parcel delineation (https://www.sciencedirect.com/science/article/pii/S0924271625000395)"
More details can be found at a recent collaborative paper "Extracting vectorized agricultural parcels from high-resolution satellite images using a Point-Line-Region interactive multitask model" published in the journal of Computers and Electronics in Agriculture. Code is available at https://github.com/mengmengli01/PLR-Net-demo/tree/main.