The Role of U-Net Variants in Semantic Segmentation of Remote Sensing Images: A Survey
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The Role of U-Net Variants in Semantic Segmentation of Remote Sensing Images: A Survey

Yiyang Liu 1*
1 School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China, 430065
*Corresponding author: aayang1048596@outlook.com
Published on 26 November 2025
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ACE Vol.210
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-567-7
ISBN (Online): 978-1-80590-568-4
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Abstract

Semantic segmentation of high-resolution remote sensing imagery is pivotal for applications such as land-cover mapping, urban planning, and environmental monitoring. Since the introduction of U-Net, numerous variants have been proposed to address challenges unique to satellite data—namely, extreme class imbalance, small-object detection, and complex scene textures. This survey systematically reviews major U-Net extensions (including U-Net++, ResUNet-a, HCANet, CCT-Net, DIResUNet, CM-UNet, TransUNet, AER-UNet and U-KAN) and additional optimization techniques such as incremental learning. This study compares their architectural innovations—e.g., nested skip connections, residual or atrous blocks, multi-scale context modules, and attention mechanisms—and summarizes reported performance on standard benchmarks (ISPRS Vaihingen, Potsdam, GID, WHDLD, DeepGlobe, and GF-2). This work also identifies key factors that drive segmentation accuracy and discusses remaining challenges and promising directions for future research, including improved generalization, reduced annotation dependency, and better trade-offs between performance and computational efficiency.

Keywords:

Deep Learning, U-Net, Remote Sensing, Semantic Segmentation

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Liu,Y. (2025). The Role of U-Net Variants in Semantic Segmentation of Remote Sensing Images: A Survey. Applied and Computational Engineering,210,41-50.

References

[1]. Lv, J., Shen, Q., Lv, M., Li, Y., Shi, L., & Zhang, P. (2023). Deep learning-based semantic segmentation of remote sensing images: a review. Frontiers in Ecology and Evolution, 11, 1201125.

[2]. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177.

[3]. Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geoscience and remote sensing magazine, 5(4), 8-36.

[4]. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing.

[5]. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

[6]. Yue, K., Yang, L., Li, R., Hu, W., Zhang, F., & Li, W. (2019). TreeUNet: Adaptive tree convolutional neural networks for subdecimeter aerial image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 1-13.

[7]. Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94-114.

[8]. Lin, W., & Li, Y. (2020). Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree.  Remote Sensing,   12(5), 783. https: //doi.org/10.3390/rs12050783

[9]. Fu, Z., Sun, Y., Fan, L., & Han, Y. (2018). Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy.  Remote Sensing,   10(8), 1289. https: //doi.org/10.3390/rs10081289

[10]. Ramos, L. T., & Sappa, A. D. (2025). Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery. Scientific Reports, 15(1), 784.

[11]. Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 11045, 3-11. https: //doi.org/10.1007/978-3-030-00889-5_1

[12]. Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94–114. https: //doi.org/10.1016/j.isprsjprs.2020.01.013

[13]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In Lecture notes in computer science (pp. 630–645). https: //doi.org/10.1007/978-3-319-46493-0_38

[14]. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

[15]. Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv: 1706.05587.

[16]. Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).

[17]. Bai, H., Cheng, J., Huang, X., Liu, S., & Deng, C. (2022). HCANet: A Hierarchical Context Aggregation Network for Semantic Segmentation of High-Resolution Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https: //doi.org/10.1109/lgrs.2021.3063799

[18]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[19]. Wang, H., Chen, X., Zhang, T., Xu, Z., & Li, J. (2022). CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images. Remote Sensing, 14(9). https: //doi.org/10.3390/rs14091956

[20]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

[21]. Priyanka, N, S., Lal, S., Nalini, J., Reddy, C. S., & Dell’Acqua, F. (2022). DIResUNet: Architecture for multiclass semantic segmentation of high resolution remote sensing imagery data. Applied Intelligence, 52(13), 15462-15482. https: //doi.org/10.1007/s10489-022-03310-z

[22]. Cui, M., Li, K., Chen, J., & Yu, W. (2023). CM-Unet: A Novel Remote Sensing Image Segmentation Method Based on Improved U-Net. IEEE Access, 11, 56994-57005. https: //doi.org/10.1109/access.2023.3282778

[23]. Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., Lungren, M. P., Zhang, S., Xing, L., Lu, L., Yuille, A., & Zhou, Y. (2024). TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med Image Anal, 97, 103280. https: //doi.org/10.1016/j.media.2024.103280

[24]. Jonnala, N. S., Siraaj, S., Prastuti, Y., Chinnababu, P., Praveen babu, B., Bansal, S., ... & Al-Mugren, K. S. (2025). AER U-Net: attention-enhanced multi-scale residual U-Net structure for water body segmentation using Sentinel-2 satellite images. Scientific Reports, 15(1), 16099.

[25]. Li, C., Liu, X., Li, W., Wang, C., Liu, H., Liu, Y., Chen, Z., & Yuan, Y. (2025). U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(5), 4652-4660. https: //doi.org/10.1609/aaai.v39i5.32491

[26]. Tasar, O., Tarabalka, Y., & Alliez, P. (2019). Incremental learning for semantic segmentation of large-scale remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3524-3537.

[27]. Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., & Breitkopf, U. (2012). The ISPRS benchmark on urban object classification and 3D building reconstruction.

[28]. Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322.

[29]. Shao, Z., Yang, K., & Zhou, W. (2018). Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset.  Remote Sensing,   10(6), 964. https: //doi.org/10.3390/rs10060964

[30]. Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., ... & Raskar, R. (2018). Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 172-181).

References

[1]. Lv, J., Shen, Q., Lv, M., Li, Y., Shi, L., & Zhang, P. (2023). Deep learning-based semantic segmentation of remote sensing images: a review. Frontiers in Ecology and Evolution, 11, 1201125.

[2]. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177.

[3]. Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geoscience and remote sensing magazine, 5(4), 8-36.

[4]. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing.

[5]. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

[6]. Yue, K., Yang, L., Li, R., Hu, W., Zhang, F., & Li, W. (2019). TreeUNet: Adaptive tree convolutional neural networks for subdecimeter aerial image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 1-13.

[7]. Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94-114.

[8]. Lin, W., & Li, Y. (2020). Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree.  Remote Sensing,   12(5), 783. https: //doi.org/10.3390/rs12050783

[9]. Fu, Z., Sun, Y., Fan, L., & Han, Y. (2018). Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy.  Remote Sensing,   10(8), 1289. https: //doi.org/10.3390/rs10081289

[10]. Ramos, L. T., & Sappa, A. D. (2025). Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery. Scientific Reports, 15(1), 784.

[11]. Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 11045, 3-11. https: //doi.org/10.1007/978-3-030-00889-5_1

[12]. Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94–114. https: //doi.org/10.1016/j.isprsjprs.2020.01.013

[13]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In Lecture notes in computer science (pp. 630–645). https: //doi.org/10.1007/978-3-319-46493-0_38

[14]. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

[15]. Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv: 1706.05587.

[16]. Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).

[17]. Bai, H., Cheng, J., Huang, X., Liu, S., & Deng, C. (2022). HCANet: A Hierarchical Context Aggregation Network for Semantic Segmentation of High-Resolution Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https: //doi.org/10.1109/lgrs.2021.3063799

[18]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[19]. Wang, H., Chen, X., Zhang, T., Xu, Z., & Li, J. (2022). CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images. Remote Sensing, 14(9). https: //doi.org/10.3390/rs14091956

[20]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

[21]. Priyanka, N, S., Lal, S., Nalini, J., Reddy, C. S., & Dell’Acqua, F. (2022). DIResUNet: Architecture for multiclass semantic segmentation of high resolution remote sensing imagery data. Applied Intelligence, 52(13), 15462-15482. https: //doi.org/10.1007/s10489-022-03310-z

[22]. Cui, M., Li, K., Chen, J., & Yu, W. (2023). CM-Unet: A Novel Remote Sensing Image Segmentation Method Based on Improved U-Net. IEEE Access, 11, 56994-57005. https: //doi.org/10.1109/access.2023.3282778

[23]. Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., Lungren, M. P., Zhang, S., Xing, L., Lu, L., Yuille, A., & Zhou, Y. (2024). TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med Image Anal, 97, 103280. https: //doi.org/10.1016/j.media.2024.103280

[24]. Jonnala, N. S., Siraaj, S., Prastuti, Y., Chinnababu, P., Praveen babu, B., Bansal, S., ... & Al-Mugren, K. S. (2025). AER U-Net: attention-enhanced multi-scale residual U-Net structure for water body segmentation using Sentinel-2 satellite images. Scientific Reports, 15(1), 16099.

[25]. Li, C., Liu, X., Li, W., Wang, C., Liu, H., Liu, Y., Chen, Z., & Yuan, Y. (2025). U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(5), 4652-4660. https: //doi.org/10.1609/aaai.v39i5.32491

[26]. Tasar, O., Tarabalka, Y., & Alliez, P. (2019). Incremental learning for semantic segmentation of large-scale remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3524-3537.

[27]. Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., & Breitkopf, U. (2012). The ISPRS benchmark on urban object classification and 3D building reconstruction.

[28]. Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322.

[29]. Shao, Z., Yang, K., & Zhou, W. (2018). Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset.  Remote Sensing,   10(6), 964. https: //doi.org/10.3390/rs10060964

[30]. Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., ... & Raskar, R. (2018). Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 172-181).

Cite this article

Liu,Y. (2025). The Role of U-Net Variants in Semantic Segmentation of Remote Sensing Images: A Survey. Applied and Computational Engineering,210,41-50.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-567-7(Print) / 978-1-80590-568-4(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.210
ISSN: 2755-2721(Print) / 2755-273X(Online)

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