Abstract Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our private Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.

中文翻译：

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来自 Landsat 8 OLI 图像的基于集成深度学习的海岸线分割方法 (WaterNet)

摘要 由于自然、城市化和人为影响，如全球变暖、人口增长和环境污染，海岸线不断变化。对沿海变化的可持续监测在沿海资源管理、环境保护和规划方面至关重要。公开可用的 Landsat 8 OLI（操作土地管理器）图像提供有关沿海地区的准确、可靠、时间和最新信息。最近，机器学习和深度学习算法的使用变得广泛。在本研究中，我们使用我们的私有 Landsat 8 OLI 卫星图像数据集创建了一种多数投票方法，即集成自动海岸线分割系统 (WaterNet) 以自动获取海岸线。为此，使用了不同的深度学习架构，即标准 U-Net、Dilated U-Net、Fractal U-Net、FC-DenseNet 和 Pix2Pix。此外，我们提出了一个新颖的框架来从 OpenStreetMap 服务创建标签数据，以创建一个名为 YTU-WaterNet 的独特数据集。根据结果​​，WaterNet 的 IoU 和 F1 分数计算为 99.59% 和 99.79%。结果表明，WaterNet 方法在从 Landsat 8 OLI 卫星图像提取海岸线方面优于其他方法。