An attention U-Net model for detection of fine-scale hydrologic streamlines
An attention U-Net model for detection of fine-scale hydrologic streamlines
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DOI：
10.1016/j.envsoft.2021.104992
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发表时间：
2021-02
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影响因子：
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通讯作者：
Zewei Xu;Shaowen Wang;L. Stanislawski;Zhe Jiang;N. Jaroenchai;Arpan Man Sainju;E. Shavers;E. L. Usery;Li Chen;Zhiyu Li;Bin Su
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作者：
Zewei Xu;Shaowen Wang;L. Stanislawski;Zhe Jiang;N. Jaroenchai;Arpan Man Sainju;E. Shavers;E. L. Usery;Li Chen;Zhiyu Li;Bin Su
地表水是人类生存和环境可持续性不可替代的资源。水文流线的精确、精细的地图表示在各个科学领域都至关重要，例如评估当前和未来水资源的数量和质量、模拟气候变化、评估农业适宜性、绘制洪水淹没图和监测环境变化。传统的方法来检测这样的流线不能充分纳入信息的复杂的三维（3D）环境的河流和陆地表面特征。这些信息对于准确地描绘流线是至关重要的。近年来，高精度激光雷达数据已变得越来越可用于获得3D信息和地表反射率。这项研究开发了一个注意力U网模型，利用高精度激光雷达数据进行精细的流线检测，并根据多种传统机器学习方法的基线评估模型结果。评估表明，注意力U网模型的平均F1得分为11.25%，优于最佳基线机器学习方法，并在分类的流线通道之间实现了更好的平滑性和连通性。这些发现表明，我们的深度学习方法可以利用高精度激光雷达数据进行精细尺度水文流线检测，从而为许多科学领域带来理想的好处。
Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.