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WangLibo1995

GeoSeg

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.

Model DevelopmentDomain-specific trainingPython
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Created 2021-10-19 · Updated 2026-10-16 · #5841 today
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WangLibo1995

WangLibo1995/GeoSeg is ranked 5,841 with 1,114 stars.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryModel Development
SubcategoryDomain-specific training
Stars1,114
Forks153
Contributors1
Created date2021-10-19
Updated date2026-10-16
Stars in 1 day1
Stars in 7 days4
Rank5,841
Previous rank11,495
Rank change5,654
7-day stars rank6,231
Previous 7-day stars rank7,088
7-day stars rank change857
Total stars rank10,264
Previous total stars rank10,260
Total stars rank change-4
CountryChina
README summary

News The code of PyramidMamba is released. Pretrained Weights of backbones can be access from Google Drive UNetFormer (accepted by ISPRS, PDF) and **UAVid dataset** are supported. ISPRS Vaihingen and Potsdam datasets are supported. Since private sharing is not allowed, you need to download the datasets from the official website and split them by **Folder Structure**. More networks are updated and the link of pretrained weights is provided. py** provides a detailed explain about **config** setting. Inference on huge RS

images are supported (inference_huge_image. Introduction **GeoSeg** is an open-source semantic segmentation toolbox based on PyTorch, pytorch lightning and timm, which mainly focuses on developing advanced 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. Supported Remote Sensing Datasets ISPRS Vaihingen and Potsdam UAVid LoveDA OpenEarthMap More datasets will be

supported in the future. Multi-scale Training and Testing Inference on Huge Remote Sensing Images Supported Networks Mamba PyramidMamba Vision Transformer UNetFormer DC-Swin BANet CNN MANet ABCNet A2FPN Folder Structure Prepare the following folders to organize this repo: Install Open the folder **airs** using **Linux Terminal** and create python environment: Install Mamba Pretrained Weights of Backbones Baidu Disk : 1234 Google Drive Data Preprocessing Download the datasets from the official website and split them yourself. **Vaihingen** Generate the training set

. Generate the masks_1024_rgb (RGB format ground truth labels) for visualization. As for the validation set, you can select some images from the training set to build it. **Potsdam** **UAVid** **LoveDA** Training "-c" means the path of the config, use different **config** to train different models. Testing "-c" denotes the path of the config, Use different **config** to test different models. 50 | Due to some random operations in the training stage, reproduced results (run once) are slightly different from

the reported in paper. Citation 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 A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images 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 A2-FPN for semantic segmentation of fine-resolution remotely sensed images 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

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WangLibo1995/GeoSeg has 1,114 stars and 153 forks.

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WangLibo1995/GeoSeg is ranked 5,841.

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WangLibo1995/GeoSeg has 1,114 stars, uses Python and belongs to Model Development.

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WangLibo1995/GeoSeg is compared with luchris429/purejaxrl, ranked 5,840 with 1,115 stars.

README

Version 2.0 (stable)

Welcome to my homepage!

News

  • The code of PyramidMamba is released.
  • I have updated this repo to pytorch 2.0 and pytorch-lightning 2.0, support multi-gpu training, etc.
  • Pretrained Weights of backbones can be access from Google Drive
  • UNetFormer (accepted by ISPRS, PDF) and UAVid dataset are supported.
  • ISPRS Vaihingen and Potsdam datasets are supported. Since private sharing is not allowed, you need to download the datasets from the official website and split them by Folder Structure.
  • More networks are updated and the link of pretrained weights is provided.
  • config/loveda/dcswin.py provides a detailed explain about config setting.
  • Inference on huge RS images are supported (inference_huge_image.py).

Introduction

GeoSeg is an open-source semantic segmentation toolbox based on PyTorch, pytorch lightning and timm, which mainly focuses on developing advanced 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.

  • Supported Remote Sensing Datasets

  • Multi-scale Training and Testing

  • Inference on Huge Remote Sensing Images

Supported Networks

Folder Structure

Prepare the following folders to organize this repo:

airs
├── GeoSeg (code)
├── pretrain_weights (pretrained weights of backbones, such as vit, swin, etc)
├── model_weights (save the model weights trained on ISPRS vaihingen, LoveDA, etc)
├── 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
│   │   ├── train_val (Merge Train and Val)
│   ├── uavid
│   │   ├── uavid_train (original)
│   │   ├── uavid_val (original)
│   │   ├── uavid_test (original)
│   │   ├── uavid_train_val (Merge uavid_train and uavid_val)
│   │   ├── train (processed)
│   │   ├── val (processed)
│   │   ├── train_val (processed)
│   ├── 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)

Install

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

conda create -n airs python=3.8
conda activate airs
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r GeoSeg/requirements.txt

Install Mamba

pip install causal-conv1d>=1.4.0
pip install mamba-ssm

Pretrained Weights of Backbones

Baidu Disk : 1234

Google Drive

Data Preprocessing

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

UAVid

python GeoSeg/tools/uavid_patch_split.py \
--input-dir "data/uavid/uavid_train_val" \
--output-img-dir "data/uavid/train_val/images" \
--output-mask-dir "data/uavid/train_val/masks" \
--mode 'train' --split-size-h 1024 --split-size-w 1024 \
--stride-h 1024 --stride-w 1024

python GeoSeg/tools/uavid_patch_split.py \
--input-dir "data/uavid/uavid_train" \
--output-img-dir "data/uavid/train/images" \
--output-mask-dir "data/uavid/train/masks" \
--mode 'train' --split-size-h 1024 --split-size-w 1024 \
--stride-h 1024 --stride-w 1024

python GeoSeg/tools/uavid_patch_split.py \
--input-dir "data/uavid/uavid_val" \
--output-img-dir "data/uavid/val/images" \
--output-mask-dir "data/uavid/val/masks" \
--mode 'val' --split-size-h 1024 --split-size-w 1024 \
--stride-h 1024 --stride-w 1024

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

Training

"-c" means the path of the config, use different config to train different models.

python GeoSeg/train_supervision.py -c GeoSeg/config/uavid/unetformer.py

Testing

"-c" denotes the path of the config, Use different config to test different models.

"-o" denotes the output path

"-t" denotes the test time augmentation (TTA), can be [None, 'lr', 'd4'], default is None, 'lr' is flip TTA, 'd4' is multiscale TTA

"--rgb" denotes whether to output masks in RGB format

Vaihingen

python GeoSeg/vaihingen_test.py -c GeoSeg/config/vaihingen/dcswin.py -o fig_results/vaihingen/dcswin --rgb -t 'd4'

Potsdam

python GeoSeg/potsdam_test.py -c GeoSeg/config/potsdam/dcswin.py -o fig_results/potsdam/dcswin --rgb -t 'lr'

LoveDA (Online Testing)

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

UAVid (Online Testing)

python GeoSeg/inference_uavid.py \
-i 'data/uavid/uavid_test' \
-c GeoSeg/config/uavid/unetformer.py \
-o fig_results/uavid/unetformer_r18 \
-t 'lr' -ph 1152 -pw 1024 -b 2 -d "uavid"

Inference on huge remote sensing image

python GeoSeg/inference_huge_image.py \
-i data/vaihingen/test_images \
-c GeoSeg/config/vaihingen/dcswin.py \
-o fig_results/vaihingen/dcswin_huge \
-t 'lr' -ph 512 -pw 512 -b 2 -d "pv"

Reproduction Results

Method Dataset F1 OA mIoU
UNetFormer UAVid - - 67.63
UNetFormer Vaihingen 90.30 91.10 82.54
UNetFormer Potsdam 92.64 91.19 86.52
UNetFormer LoveDA - - 52.97
FT-UNetFormer Vaihingen 91.17 91.74 83.98
FT-UNetFormer Potsdam 93.22 91.87 87.50

Due to some random operations in the training stage, reproduced results (run once) are slightly different from the reported in paper.

Citation

If you find this project useful in your research, please consider citing：

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.

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