---
license: mit
language:
- en
pipeline_tag: image-segmentation
tags:
- image-segmentation
- remote-sensing
---

# PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images

<!-- Provide a quick summary of what the model is/does. -->

PC-SAM is a fine-grained interactive road segmentation model for high-resolution remote sensing images, it supports both automatic road segmentation and interactive segmentation refinement. By using point prompts, users can correct segmentation errors locally and obtain more accurate road segmentation masks.


## Model Details

### Model Description

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- **License:** MIT
- **Finetuned from model:** SAM

### Model Sources

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- **Repository:** [PC-SAM Github](https://github.com/Cyber-CCOrange/PC-SAM)
- **Paper:** [PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images](https://doi.org/10.48550/arXiv.2604.00495)

## Uses/How to Get Started with the Model

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Please refer to the GitHub page of PC-SAM.

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

@misc{lv2026pcsampatchconstrainedfinegrainedinteractive,
      title={PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images}, 
      author={Chengcheng Lv and Rushi Li and Mincheng Wu and Xiufang Shi and Zhenyu Wen and Shibo He},
      year={2026},
      eprint={2604.00495},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.00495}, 
}