GeoSeg is an open-source semantic segmentation toolbox based on PyTorch, pytorch lightning and timm, which mainly focuses on remote sensing image segmentation.
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
│ ├── vaihingen
│ │ ├── train_images (original)
│ │ ├── train_masks (original)
│ │ ├── test_images (original)
│ │ ├── test_masks (original)
│ │ ├── test_masks_eroded (original)
│ │ ├── train (processed)
│ │ ├── test (processed)
│ ├── potsdam (the same with vaihingen)
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
Baidu Disk : 1234
Download the datasets from the official website and split them yourself.
Vaihingen
Generate the training set.
python GeoSeg/tools/vaihingen_patch_split.py \
--img-dir "data/vaihingen/train_images" \
--mask-dir "data/vaihingen/train_masks" \
--output-img-dir "data/vaihingen/train/images_1024" \
--output-mask-dir "data/vaihingen/train/masks_1024" \
--mode "train" --split-size 1024 --stride 512
Generate the testing set.
python GeoSeg/tools/vaihingen_patch_split.py \
--img-dir "data/vaihingen/test_images" \
--mask-dir "data/vaihingen/test_masks_eroded" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024" \
--mode "val" --split-size 1024 --stride 1024 \
--eroded
Generate the masks_1024_rgb (RGB format ground truth labels) for visualization.
python GeoSeg/tools/vaihingen_patch_split.py \
--img-dir "data/vaihingen/test_images" \
--mask-dir "data/vaihingen/test_masks" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024_rgb" \
--mode "val" --split-size 1024 --stride 1024 \
--gt
As for the validation set, you can select some images from the training set to build it.
Potsdam
python GeoSeg/tools/potsdam_patch_split.py \
--img-dir "data/potsdam/train_images" \
--mask-dir "data/potsdam/train_masks" \
--output-img-dir "data/potsdam/train/images_1024" \
--output-mask-dir "data/potsdam/train/masks_1024" \
--mode "train" --split-size 1024 --stride 1024 --rgb-image
python GeoSeg/tools/potsdam_patch_split.py \
--img-dir "data/potsdam/test_images" \
--mask-dir "data/potsdam/test_masks_eroded" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024" \
--mode "val" --split-size 1024 --stride 1024 \
--eroded --rgb-image
python GeoSeg/tools/potsdam_patch_split.py \
--img-dir "data/potsdam/test_images" \
--mask-dir "data/potsdam/test_masks" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024_rgb" \
--mode "val" --split-size 1024 --stride 1024 \
--gt --rgb-image
python GeoSeg/train_supervision.py -c GeoSeg/config/uavid/unetformer.py
Use different config to train different models.
For example:
python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/dcswin.py -o fig_results/loveda/dcswin_val --rgb --val -t 'd4'
If you find this project useful in your research, please consider citing：
- UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
- Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
- ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
- Multiattention network for semantic segmentation of fine-resolution remote sensing images
- BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation
This project is built upon GeoSeg. Many thanks to their great work!