r0na1d99/geoseg

A toolbox for semantic segmentation of remote sensing images.

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Introduction

GeoSeg is an open-source semantic segmentation toolbox based on PyTorch, pytorch lightning and timm, which mainly focuses on using advanced CNNs and Vision Transformers for remote sensing image segmentation.

Major Features

• Unified Benchmark

  we provide a unified training script for various segmentation methods.

• Simple and Effective

  Thanks to pytorch lightning and timm , the code is easy for further development.

• Support More Remote Sensing Datasets

  • ISPRS Vaihingen and Potsdam
  • UAVid
  • LoveDA
  • WHU building
  • Inria Aerial Image Labelling
  • More datasets will be supported in the future.

• Multi-scale Training and Testing

• Inference on Huge Remote Sensing Images

Supported Networks

• Vision Transformer

  • UNetFormer
  • DC-Swin
  • BANet

• CNN

  • MANet
  • ABCNet
  • A2FPN

Folder Structure

Prepare the following folders to organize this repo:

airs
├── GeoSeg (code)
├── pretrain_weights (save the pretrained weights like vit, swin, etc)
├── model_weights (save the model weights)
├── fig_results (save the masks predicted by models)
├── lightning_logs (CSV format training logs)
├── data
│   ├── LoveDA
│   │   ├── Train
│   │   │   ├── Urban
│   │   │   │   ├── images_png (original images)
│   │   │   │   ├── masks_png (original masks)
│   │   │   │   ├── masks_png_convert (converted masks used for training)
│   │   │   │   ├── masks_png_convert_rgb (original rgb format masks)
│   │   │   ├── Rural
│   │   │   │   ├── images_png 
│   │   │   │   ├── masks_png 
│   │   │   │   ├── masks_png_convert
│   │   │   │   ├── masks_png_convert_rgb
│   │   ├── Val (the same with Train)
│   │   ├── test
│   ├── uavid
│   │   ├── uavid_train (original)
│   │   ├── uavid_val (original)
│   │   ├── uavid_test (original)
│   │   ├── train (processed)
│   │   ├── val (processed)
│   ├── vaihingen
│   │   ├── train_images (original)
│   │   ├── train_masks (original)
│   │   ├── train_masks_eroded (original)
│   │   ├── val_images (original)
│   │   ├── val_masks (original)
│   │   ├── val_masks_eroded (original)
│   │   ├── test_images (original)
│   │   ├── test_masks (original)
│   │   ├── test_masks_eroded (original)
│   │   ├── train (processed)
│   │   ├── val (processed)
│   │   ├── test (processed)
│   ├── potsdam (the same with vaihingen)

Install

Open the folder airs using Linux Terminal and create python environment:

conda create -n airs python=3.8
conda activate airs

conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r GeoSeg/requirements.txt

Data Preprocessing

LoveDA

python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Train/Rural/masks_png --output-mask-dir data/LoveDA/Train/Rural/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Train/Urban/masks_png --output-mask-dir data/LoveDA/Train/Urban/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Val/Rural/masks_png --output-mask-dir data/LoveDA/Val/Rural/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Val/Urban/masks_png --output-mask-dir data/LoveDA/Val/Urban/masks_png_convert

More datasets are updating.

Training

LoveDA

python GeoSeg/train_supervision.py -c GeoSeg/config/loveda/dcswin.py

Validation

LoveDA

python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/dcswin.py -o fig_results/loveda/dcswin_val --rgb --val -t 'd4'

Testing

LoveDA

python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/dcswin.py -o fig_results/loveda/dcswin_test --rgb -t 'd4'

Pretrained Weights

Baidu : 1234

Citation

If you find this project useful in your research, please consider citing our papers.

Acknowledgement

We wish GeoSeg could serve the growing research of remote sensing by providing a unified benchmark and inspiring researchers to develop their own segmentation networks. Many thanks the following projects's contributions to GeoSeg.

• pytorch lightning
• timm
• pytorch-toolbelt
• ttach
• catalyst
• mmsegmentation