Wetlands serve many important ecosystem services, yet the United States lacks up-to-date, high-resolution wetland inventories. New, automated techniques for developing wetland segmentation maps from high-resolution aerial imagery can improve our understanding of the location and amount of wetlands. We assembled training and testing data sets (patch sizes of 28 × 28 m² and 56 × 56 m²) of high-resolution aerial imagery of wetlands using Illinois Natural History Survey wetland location data and National Agricultural Imagery Project data. Each patch was labeled as wetland or non-wetland. To augment these data sets with additional information, we incorporated digital surface and digital terrain models and topographic wetness index data in the same two patch sizes. Subsequently, we evaluated convolutional neural network (CNN) and Gaussian process-based machine learning methods to produce wetland segmentation maps. We developed the best performing method into a new CNN algorithm, WetSegNet. It exhibited an area under the curve of 98% when used with 56 × 56 m² patch sizes. WetSegNet developed reliable wetland segmentation maps in test cases in which wetlands would have gone undetected using only the National Land Cover Database. The development of WetSegNet exemplifies the types of data sets and methods that are needed to accelerate the use of high-resolution aerial imagery towards an improved understanding of wetlands. This algorithm could be used by state and federal agencies or other groups to identify wetlands with higher accuracy and at a finer scale than previously possible.

湿地为许多重要的生态系统服务提供服务，但美国缺乏最新的、高分辨率的湿地清单。用于从高分辨率航空影像开发湿地分割图的新自动化技术可以提高我们对湿地位置和数量的理解。我们组装了训练和测试数据集（补丁大小为 28 × 28 m ²和 56 × 56 m ²) 使用伊利诺伊州自然历史调查湿地位置数据和国家农业影像项目数据的高分辨率湿地航空影像。每个补丁被标记为湿地或非湿地。为了增加这些数据集的附加信息，我们在相同的两个补丁大小中加入了数字表面和数字地形模型以及地形湿度指数数据。随后，我们评估了卷积神经网络 (CNN) 和基于高斯过程的机器学习方法来生成湿地分割图。我们将性能最佳的方法开发成一种新的 CNN 算法 WetSegNet。与 56 × 56 m ²配合使用时，其曲线下面积为 98%补丁大小。WetSegNet 在测试案例中开发了可靠的湿地分割图，其中仅使用国家土地覆盖数据库就不会检测到湿地。WetSegNet 的发展举例说明了加速使用高分辨率航空影像以更好地了解湿地所需的数据集和方法的类型。州和联邦机构或其他团体可以使用该算法以比以前更高的精度和更精细的规模识别湿地。