PC SAM
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,...
Source du modèle
- Type
- Other
- Taille du fichier
- 421–485 MB
- Version
- vCH
- Mis à jour
- 4 nov. 2026
- Source
- Hugging Face
Description de la source
PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images
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
- License: MIT
- Finetuned from model: SAM
Model Sources
- Repository: PC-SAM Github
- Paper: PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images
Uses/How to Get Started with the Model
Please refer to the GitHub page of PC-SAM.
Citation
@article{lv2026pc,
title={PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images},
author={Lv, Chengcheng and Li, Rushi and Wu, Mincheng and Shi, Xiufang and Wen, Zhenyu and He, Shibo},
journal={arXiv preprint arXiv:2604.00495},
year={2026}
}
Sources
1 source
Sources
1 sourceVérifié 4 nov.
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Artefacts du modèle
3 artefacts
Artefacts du modèle
3 artefactsExtraits de sources
2 extraits
Extraits de sources
2 extraits--- 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 <!-- Provide a longer summary of what this model is. --> - **License:** MIT - **Finetuned from model:** SAM ### Model Sources <!-- Provide the basic links for the model. --> - **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 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> 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. --> ``` @article{lv2026pc, title={PC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images}, author={Lv, Chengcheng and Li, Rushi and Wu, Mincheng and Shi, Xiufang and Wen, Zhenyu and He, Shibo}, journal={arXiv preprint arXiv:2604.00495}, year={2026} } ```
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo Cyber-CCOrange/PC-SAM
