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Semantic Segmentation for Remote Sensing
| Date | Stars |
|---|---|
| 2026-07-24 | 800 |
| 2026-07-25 | 800 |
| 2026-07-28 | 800 |
| 2026-07-30 | 800 |
| 2026-08-06 | 800 |
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# Semantic Segmentation for Remote Sensing
This repo is the PyTorch implementation of some works related to remote sensing tasks.
## Methods
> **Single-modal Segmentation**:
EVSR (submitted to TIP).
[DeepKANSeg](https://ieeexplore.ieee.org/abstract/document/11615317) (IEEE TGRS 2026).
[SAM_RS](https://ieeexplore.ieee.org/abstract/document/10636322) (IEEE TGRS 2024) (**ESI Highly Cited Paper**).
[RS^3Mamba](https://ieeexplore.ieee.org/abstract/document/10556777) (IEEE GRSL 2024) (**ESI Hot Paper**).
> **Unsupervised Domain Adaptation (UDA)**:
[GLGAN](https://ieeexplore.ieee.org/document/10721444) (IEEE TGRS 2024).
[MBATA_GAN](https://ieeexplore.ieee.org/abstract/document/10032584/) (IEEE TGRS 2023) (**ESI Highly Cited Paper**).
> **Multimodal Fusion**:
[MFNet](https://ieeexplore.ieee.org/abstract/document/11063320) (IEEE TGRS 2025) (**ESI Highly Cited Paper**).
[ASMFNet](https://ieeexplore.ieee.org/document/10736654) (IEEE JSTARS 2024).
[FTransUNet](https://ieeexplore.ieee.org/document/10458980) (IEEE TGRS 2024) (**ESI Hot Paper**).
[CMFNet](https://ieeexplore.ieee.org/abstract/document/9749821/) (IEEE JSTARS 2022) (**ESI Highly Cited Paper**).
[MSFNet](https://ieeexplore.ieee.org/abstract/document/9883789) (IEEE IGARSS 2022).
## Reference
For UDA:
* [TransGAN](https://github.com/VITA-Group/TransGAN)
* [Advent](https://github.com/valeoai/ADVENT)
* [AdaSegNet](https://github.com/wasidennis/AdaptSegNet)
For Segment Anything Model:
* [Medical-SAM-Adapter](https://github.com/MedicineToken/Medical-SAM-Adapter/tree/main)
* [SAM](https://github.com/facebookresearch/segment-anything)
For Semantic Segmentation:
* [TransUNet](https://github.com/Beckschen/TransUNet)
* [UNetformer](https://github.com/WangLibo1995/GeoSeg)
* [UCTransNet](https://github.com/McGregorWwww/UCTransNet)
* [Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
* [Swin-Unet](https://github.com/HuCaoFighting/Swin-Unet)
* [v-FuseNet](https://github.com/nshaud/DeepNetsForEO)
* [FuseNet](https://github.com/MehmetAygun/fusenet-pytorch)
For Semantic Segmentation based on Mamba:
* [VMamba](https://github.com/MzeroMiko/VMamba)
* [VM-UNet](https://github.com/JCruan519/VM-UNet)
* [Swin-UMamba](https://github.com/JiarunLiu/Swin-UMamba)
## Datasets
Datasets including ISPRS Potsdam, ISPRS Vaihingen, loveDA can be downloaded [here](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#prepare-datasets).
Dataset MMHunan can be downloaded [here](https://github.com/LauraChow/HunanMultimodalDataset).
## Scripts
There are some scripts in utils folder including: split images, merge images, draw loss curves.
## Other Works
There are some other works in our group:
> **Scene Classification**: [DF4LCZ](https://github.com/ctrlovefly/DF4LCZ) (IEEE TGRS 2024).
> **Change Detection**: [GCD-DDPM](https://github.com/udrs/GCDDDPM) (IEEE TGRS 2024) (**ESI Highly Cited Paper**), [GVLM](https://github.com/zxk688/GVLM) (ISPRS 2023) (**ESI Highly Cited Paper**).
> **Super-resolution**: [ASDDPM](https://github.com/littlebeen/ASDDPM-Adaptive-Semantic-Enhanced-DDPM) (IEEE JSTARS 2024), [GCRDN](https://github.com/zxk688/GCRDN) (IEEE JSTARS 2023).
## Citations
If these codes are helpful for your study, please cite:
```
@ARTICLE{wang2026KAN,
author={Wang, Ziyao and Hu, Yin and Zhang, Xiaokang and Ye, Yuanxin and Ma, Xianping and Pun, Man-On},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Kolmogorov-Arnold Network for Remote Sensing Image Semantic Segmentation},
year={2026},
pages={1-17}
}
@ARTICLE{ma2024manet,
author={Ma, Xianping and Zhang, Xiaokang and Pun, Man-On and Huang, Bo},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={A Unified Framework With Multimodal Fine-Tuning for Remote Sensing Semantic Segmentation},
year={2025},
volume={63},
number={},
pages={1-15}
}
@article{ma2024frequency,
author={Ma, Xianping and Zhang, Xiaokang and Ding, Xingchen Excerpt of 7,003 characters
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