(AAAI 2025) Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation

by Chengyang Ye, Yunzhi Zhuge, Pingping Zhang*

Paper

Introduction

New Task: Open-Vocabulary RSI Semantic Segmantation (OVRSISS)

• OVRSISS aims at segmenting arbitrary semantic class in RSI domain.
• OVRSISS methods need no finetuning adapting to new classes.

New Dataset: LandDiscover50K (LD50K)

• 51,846 RSI images covering 40 diverse semantic classes.
• Building upon established RSISS datasets, including OEM, LoveDA, DeepGlobe, SAMRS. For further details, please refer to the paper.
• Elevates the performance of OVRSISS by providing large-scale, multi-domain, multi-granularity RSI images with comprehensive class annotations.

New Approach: GSNet (Generalist and Specialist Network)

• Dual-Stream Image Encoder
  • Generalist: CLIP backbone for open-vocabulary recognition.
  • Specialist: RSI backbone for domain-specific expertise.
• Query-Guided Feature Fusion
  • Efficient Fusion of Generalist and Specialist Features under the guidance of text-based queries.
• Residual Information Preservation Decoder
  • Aggregates multi-source features for more accurate mask predictions.
  • Detail refinement and backbone regularization.

Prepare datasets

Training Dataset: LD50K

• Download LandDiscover50K from this hugginface repo
• Extract the complete zip file

  7z x LD50KSplit.zip
  

Testing Datasets: FloodNet, FLAIR, FAST, Potsdam

• We provide pre-processed test images and masks for users' convenience. Download them from here.
• For pre-process of the testing datasets, check the paper.

Expected data strcture

$DETECTRON2_DATASETS  
├── LandDiscover50K  
│   ├── GT_ID  
│   └── TR_Image  
├── FAST  
│   └── val  
│       ├── images  
│       └── semlabels  
├── PotsdamSplit  
│   ├── ann_dir  
│   └── img_dir  
├── FLAIR  
│   └── test  
│       ├── image  
│       └── mask  
├── FloodNet  
│   └── val+test  
│       ├── img 
│       └── lbl  

Installation

An example of installation is shown below:

git clone https://github.com/yecy749/GSNet.git
cd GSNet
conda create -n gsnet python=3.8
conda activate gsnet
conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

• Linux with Python ≥ 3.8 required
• Ensure that the versions of PyTorch, TorchVision, and Detectron2 are compatible. For more information, refer to pytorch.org and the Detectron2 install guide.

Training and Evaluation

• Download the pretrained Specialist RSI Backbone weights.
• Specify root path of datasets & RSIB weight path.
• Command for Training

  sh scripts/train.sh configs/vitb_384.yaml [NUM_GPUs] [TRAIN_RESULTS_DIR]
  

• We provide pretrained weights for our model reported in the paper. Reproduction can be made through command line scripts below.
• Command for Evaluation

  sh scripts/eval.sh configs/vitb_384.yaml [NUM_GPUs] [EVAL_RESULTS_DIR] \
  MODEL.WEIGHTS [MODEL_WEIGHTS_PATH]
  

• An example of training and evaluation command is provided in scripts/run.sh.

Acknowledgement

We sincerely appreciate the invaluable contributions of numerous open-source projects and datasets that have supported our work, including but not limited to DETECTRON2, CAT-SEG, SAMRS, OEM, LoveDA, DeepGlobe, FloodNet, ISPRS Potsdam, FLAIR.

Citing GSNet

If you find GSNet helpful in your research, please consider citing:

@inproceedings{ye2025GSNet,
  title={Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation},
  author={Ye, Chengyang and Zhuge, Yunzhi and Zhang, Pingping},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}