Open Access
| Issue |
E3S Web Conf.
Volume 711, 2026
2026 2nd International Conference on Environmental Monitoring and Ecological Restoration (EMER 2026)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 7 | |
| Section | Environmental Monitoring and Assessment | |
| DOI | https://doi.org/10.1051/e3sconf/202671101001 | |
| Published online | 19 May 2026 | |
- Zhang, G., Zhao, F., Wang, X., et al. (2025). Deep learning-driven super-resolution and DenseCLIP-based semantic segmentation for coral reef mapping. In OCEANS 2025 Brest (pp. 1–4). IEEE. [Google Scholar]
- Chiang, J. Y., & Chen, Y. C. (2012). Underwater image enhancement by wavelength compensation and dehazing. IEEE Transactions on Image Processing, 21(4), 1756–1769. [Google Scholar]
- Shao, X., Chen, H., Zhao, F., et al. (2026). Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning. Marine Pollution Bulletin, 223, 119054. [Google Scholar]
- Zhao, F., He, Y., Song, J., et al. (2025). Smart UAV-assisted blueberry maturity monitoring with Mamba-based computer vision. Precision Agriculture, 26(4), 56. [Google Scholar]
- Chen, J., Li, P., Lee, J., et al. (2025). Optimization of oblique drone photogrammetry for avoiding sun glint in submerged seagrass mapping. Estuarine, Coastal and Shelf Science, 109356. [Google Scholar]
- Zhao, F., Shao, X., Wang, J., et al. (2025). A novel underwater Holothurians monitoring system using consumer-grade amphibious UAV with Mamba-based super-resolution reconstruction and enhanced YOLOv10. Marine Environmental Research, 107510. [Google Scholar]
- González-Rivero, M., et al. (2020). Monitoring of coral reefs using artificial intelligence: A feasible and scalable approach. Remote Sensing in Ecology and Conservation, 6(4), 472–482. [Google Scholar]
- Li, C., Guo, J., Guo, C., & Cong, R. (2019). WaterNet: An adaptive deep network for underwater image enhancement. IEEE Transactions on Image Processing, 28(11), 5580–5595. [Google Scholar]
- Li, C., Anwar, S., & Porikli, F. (2021). Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recognition, 114, 107858. [Google Scholar]
- Li, C., Guo, C., Ren, W., Cong, R., & Hou, J. (2020). Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE Transactions on Image Processing, 29, 9380–9395. [Google Scholar]
- Goodfellow, I., Pouget-Abadie, J., Mirza, S., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS), 27. [Google Scholar]
- Li, M., Zhang, H., Gruen, A., et al. (2025). A survey on underwater coral image segmentation based on deep learning. Geo-spatial Information Science, 28(2), 472–496. [Google Scholar]
- Mittal, S., Tatarchenko, M., & Brox, T. (2021). Semi-supervised semantic segmentation with high- and low-level consistency. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(12), 4311–4324. [Google Scholar]
- Oliver, A., et al. (2018). Realistic evaluation of deep semi-supervised learning algorithms. Advances in Neural Information Processing Systems (NeurIPS), 31. [Google Scholar]
- Raine, S., Marchant, R., Kusy, B., Maire, F., & Fischer, T. (2022). Point label aware superpixels for multi-species segmentation of underwater imagery. arXiv preprint, arXiv:2201.09997. [Google Scholar]
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 (pp. 234–241). [Google Scholar]
- Sohn, K., Berthelot, D., Li, C. L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020). FixMatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems (NeurIPS), 33. [Google Scholar]
- Tarvainen, A., & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems (NeurIPS), 30, 1195–1204. [Google Scholar]
- Valdebenito Maturana, C. N., Sandoval Orozco, A. L., & García Villalba, L. J. (2023). Exploration of metrics and datasets to assess the fidelity of images generated by generative adversarial networks. Applied Sciences, 13(3), 1842. [Google Scholar]
- Wang, J., Mizuno, K., Tabeta, S., et al. (2025). Multi-dataset-integrated Coral-Lab segmentation with enhanced towed camera array for rapid large-scale coral reef monitoring and mapping. International Journal of Applied Earth Observation and Geoinformation, 143, 104819. [Google Scholar]
- Williams, I. D., Couch, C. S., Beijbom, O., et al. (2019). Leveraging automated image analysis to monitor coral reef ecosystems. Environmental Monitoring and Assessment, 191(8), 466. [Google Scholar]
- Yu, X., Ouyang, B., & Principe, J. C. (2021). Coral image segmentation with point-supervision via latent Dirichlet allocation with spatial coherence. Journal of Marine Science and Engineering, 9(2), 157. [Google Scholar]
- Alaluf, Y., Patashnik, O., Wu, Z., Zamir, A., Shechtman, E., Lischinski, D., & Cohen-Or, D. (2022). Third time’s the charm? Image and video editing with StyleGAN3. In European Conference on Computer Vision (ECCV) (pp. 204–220). Springer Nature Switzerland. [Google Scholar]
- Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. In 2010 20th International Conference on Pattern Recognition (pp. 2366–2369). IEEE. [Google Scholar]
- Yu, Y., Zhang, W., & Deng, Y. (2021). Frechet inception distance (FID) for evaluating GANs. China University of Mining Technology Beijing Graduate School, 3(11). [Google Scholar]
- Bi ή kowski, M., Sutherland, D. J., Arbel, M., & Gretton, A. (2018). Demystifying MMD GANs. arXiv preprint, arXiv:1801.01401. [Google Scholar]
- Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 658–666). [Google Scholar]
- Benenson, R., Mathias, M., Timofte, R., & Van Gool, L. (2012). Pedestrian detection at 100 frames per second. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2903 - 2910). IEEE. [Google Scholar]
- Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019). Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems (NeurIPS), 32. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

