Awesome

</head> <body> <h1>Fine-Tuning SAM (Segment anything) for River Water Segmentation</h1> <h2>Overview</h2> <p> This repository presents the Python code for fine-tuning the Segment Anything Model (SAM) to perform river water segmentation from close-range remote sensing imagery. This work is based on our paper published in IEEE Access: </p> <p> <strong>A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann</strong>, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," IEEE Access, 2024. <a href="https://ieeexplore.ieee.org/document/10493013">IEEE Access</a> </p>

The easy-to-use and adaptable code for river water and other segmentation tasks and use for other remote sensing datasets:

Try it in Colab:

The LuFI-RiverSNAP.v1 (river water segmentation) Dataset in Google Drive:

Test Image 1

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> </head> <body> <p>Some examples of river water segmentation results on the LuFI-RiverSnap.<i>v1</i>. (a) Images and segmentation results generated by (b) U-Net<sub>(ResNet50)</sub>, (c) PSPNet<sub>(ResNet50)</sub>, (d) DeeplabV3+<sub>(ResNet50)</sub>, (e) PAN<sub>(ResNet50)</sub>, (f) LinkNet<sub>(ResNet50)</sub>, and (g) SAM were used as DL models for river water segmentation. Green: False Positives (<span style="color: green;">FP</span>) detection, Pink: False Negatives (<span style="color: pink;">FN</span>) detection, Blue: correct detection of river water.</p> </body> </html>

Try it in Colab:</br></br>

Please also follow and read the reference codes we created for our fine-tuning SAM based on.

<li>Original SAM Code: <a href="https://github.com/facebookresearch/segment-anything">GitHub - Segment Anything</a></li> <li>Fine-Tuning Tutorial: <a href="https://encord.com/blog/learn-how-to-fine-tune-the-segment-anything-model-sam/">Encord Blog - Fine-Tune SAM</a></li> </ul>

Test Image 2 {Some examples of river water segmentation results on the LuFI-RiverSnap.\textit{v}1. (a) Images and segmentation results generated by (b) MobileSAM (TinyViT), (c) SAM (ViT-B), (d) and SAM (ViT-L)}

<h3>Dataset</h3> <p>The LuFI-RiverSNAP.v1 dataset for river water segmentation is available on multiple platforms:</p> <ul> <li><a href="https://www.kaggle.com/datasets/arminmoghimi/lufi-riversnap">Kaggle: LuFI-RiverSNAP</a></li> <li><a href="https://www2.isprs.org/commissions/comm3/icwg-3-4a/datasets/">ISPRS ICWG III/IVa "Disaster Management" Datasets</a></li> <li><a href="https://ieee-dataport.org/documents/lufi-riversnap-river-water-segmentation">IEEE DataPort: LuFI-RiverSNAP</a></li> </ul>

Try it in Colab:</br></br>

Cite

Please cite the following papers if they help your research. You can use the following BibTeX entry:

@article{moghimi2024comparative,
  title={A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery},
  author={Moghimi, Armin and Welzel, Mario and Celik, Turgay and Schlurmann, Torsten},
  journal={IEEE Access},
  year={2024},
  doi={https://doi.org/10.48550/arXiv.2304.02643},
  publisher={IEEE}
}

A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," in IEEE Access, doi: 10.1109/ACCESS.2024.3385425. https://ieeexplore.ieee.org/document/10493013

@inproceedings{kirillov2023segment,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4015--4026},
  doi={https://doi.org/10.48550/arXiv.2304.02643},
  year={2023}
}

</code> </pre> <h2>Contact</h2> <p> For any queries or contributions, feel free to contact us. </p> </body>