Dinghow/UGBS-pytorch

The official pytorch implementation of Exploring the User Guidance for More Accurate Building Segmentation from High-Resolution Remote Sensing Images

Exploring the User Guidance for More Accurate Building Segmentation

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.

1. Requirements

• 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

2. Dataset

Segmentation

• 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
  

3. Model Zoo

Segmentation

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

4. Inference

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:

1. To evaluate segmentation methods with the metrics of CVNet (IoU, WCov, BF-score and Dice), you shoud use seg_tool/evaluate_cvnet.sh

5. License

This project is under the MIT license.