This repo is the official PyTorch implementation of Exploring the User Guidance for More Accurate Building Segmentation from High-Resolution Remote Sensing Images. Since this work is still under review, we only provide the test code and related models, and the full code will be released soon.
-
Hardware: 4-8 GPUs (better with >=11G GPU memory)
-
Software: PyTorch>=1.0.0, Python3, tensorboardX, and so on.
-
to install tensorboardX, you could do as follows:
pip install tensorboardX pip install tensorboard
-
install python packages:
pip install -r requirements.txt
- SpaceNet (Las Vegas): processed test set
- Inria-building dataset: dataset, dataset split file
Segmentation data structure
DATA_ROOT ├── vegas │ ├── vegas_trainval_img_224_jpg │ ├── vegas_trainval_label_224_png_01 │ ├── vegas_test_img_224_jpg │ ├── vegas_test_label_224_png_01 ├── Inria_dataset │ ├── inria │ │ ├── img │ │ ├── inria_split │ │ ├── mask_all │ │ ├── mask_one │ │ ├── polygons.csv
train: SpaceNet (Las Vegas)
Annotations | Models | IoU | BF-score | B-IoU | Notes |
---|---|---|---|---|---|
Bounding box | gdrive | 93.7 | 80.7 | 39.9 | |
Extreme points | gdrive | 95.3 | 87.3 | 48.5 | |
Inside-outside points | gdrive | 95.4 | 86.3 | 49.4 |
train: Inria-building dataset
Annotations | Models | IoU | WCov | B-Fscore | Dice | Notes |
---|---|---|---|---|---|---|
Bounding box | gdrive | 92.1 | 92.2 | 84.3 | 95.9 | |
Extreme points | gdrive | 93.1 | 93.1 | 86.9 | 96.4 | |
Inside-outside points | gdrive | 92.8 | 92.9 | 86.0 | 96.3 |
Run on one GPU to evaluate the model, the examples are as follow:
Run the inference job:
sh {tool_name}/evaluate_banet.sh {dataset} {task_name}
An example of testing our method on Vegas:
sh seg_tool/evaluate_banet.sh vegas banet_dextr_res101
Note:
- To evaluate segmentation methods with the metrics of CVNet (IoU, WCov, BF-score and Dice), you shoud use
seg_tool/evaluate_cvnet.sh
This project is under the MIT license.