🌍 S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing

This repository provides the official implementation, datasets, and checkpoints for S5, the first scalable semi-supervised semantic segmentation framework in remote sensing.

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🎯 Introduction

• Dataset (RS4P-1M): We curate RS4P-1M, a large-scale dataset with 1 million unlabeled remote sensing images with pseudo-labels.
• S4P (Semi-supervised Semantic Segmentation Pre-training): Extends traditional semi-supervised semantic segmentation (S4) into large-scale pre-training, leveraging RS4P-1M with FixMatch to learn generalizable representations.
• MoE-MDF (Mixture-of-Experts Multi-Dataset Fine-tuning): A multi-dataset fine-tuning strategy with shared + task-specific experts, enabling efficient adaptation across RS benchmarks with minimal overhead.

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✅ To-do List

• Release checkpoints of S5 (ViT-B/L/H).
• Release pre-training codes and configs for S4P.
• Release RS4P-1M dataset.
• Release codes and configs for downstream tasks (Object Detection, Semantic Segmentation).

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🔥 News

• 2025.08: Paper released on arXiv.
• 2025.08: We released the S4P code and the pretrained weights (ViT-B/L). Download link: Baidu Netdisk, extraction code: huuh.
• 2025.09: We released the fine-tuning code and weights for remote sensing semantic segmentation (ViT-B/L). Download link: Baidu Netdisk, extraction code: 4xvx.
• 2025.09: We released the fine-tuning code and weights for remote sensing rotated object detection (ViT-B/L). Download link: Baidu Netdisk, extraction code: y9s3.

📚 Contents

• Performance
• RS4P-1M
• S4P
• MoE-MDF
• License

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📊 Performance

We compare S5 with state-of-the-art remote sensing foundation models (RSFMs) on semantic segmentation and object detection benchmarks.

Method	Backbone	Params Det (M, Single)	Params Det (M, Multiple)	DIOR-R	DOTA-v2	Params Seg (M, Single)	Params Seg (M, Multiple)	Vaihingen	Potsdam	LoveDA	OpenEarthMap
RVSA	ViT-B + RVSA	111.2	222.4	68.06	55.22	103.2	412.8	78.49	91.58	52.44	66.63
GFM	Swin-B	104.1	208.2	67.67	59.15	96.9	387.6	79.61	91.85	54.98	67.78
Scale-MAE	ViT-L	334.6	669.2	66.47	56.97	327.4	1309.6	78.64	91.54	53.67	68.54
SAMRS	ViT-B + RVSA	-	-	-	-	103.2	412.8	78.73	91.69	53.04	67.37
SatMAE++	ViT-L	334.6	669.2	66.82	55.60	327.4	1309.6	78.80	91.64	52.82	65.62
BillionFM	ViT-G	996.9	1993.9	73.62	58.69	990.9	-	-	92.58	54.40	-
OREOLE	ViT-G	996.9	-	71.31	-	990.9	-	-	92.20	54.00	-
MTP	ViT-L + RVSA	334.6	669.2	74.54	58.41	327.4	1309.6	80.62	92.47	54.16	69.04
MA3E	ViT-B	111.2	-	71.82	-	103.2	-	-	91.50	-	-
SelectiveMAE	ViT-L	334.6	669.2	71.75	57.84	327.4	1309.6	80.45	92.78	54.31	69.30
S5 (Ours)	ViT-B	111.2	138.3	72.95	57.20	103.2	160.4	79.85	92.40	54.02	68.65
S5 (Ours)	ViT-L	334.6	377.8	75.21	59.71	327.4	435.0	80.72	92.78	55.67	69.66
S5 (Ours)	ViT-H	671.7	730.0	75.30	59.89	663.4	824.5	80.85	92.97	55.65	70.02

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🚀 RS4P-1M

RS4P-1M is a large-scale optical remote sensing dataset for semi-supervised semantic segmentation pre-training, comprising one million images with high-quality pseudo-labels.

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🚀 S4P

⚙️ Installation for Pretraining

Please install the pretraining dependencies in S5/requirements.txt:

# Optionally create a conda environment
conda create -n s5_seg python=3.10 -y
conda activate s5_seg
# Install PyTorch
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
# Install other dependencies
pip install -r requirements.txt

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🚀 MoE-MDF

Unified fine-tuning across multiple RS benchmarks with shared + task-specific experts. Supports semantic segmentation (Vaihingen, Potsdam, LoveDA, OpenEarthMap) and object detection (DIOR-R, DOTA-v2.0).

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⭐ Citation

If you find S5 helpful, please consider giving this repo a ⭐ and citing:

@article{S5,
  title={S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing},
  author={Liang Lv and Di Wang and Jing Zhang and Lefei Zhang},
  journal={arXiv preprint arXiv:2508.12409},
  year={2025}
}

🤝 License

Apache License 2.0. Please check LICENSE.md for details.

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