Automated and accurate wetland identification algorithms are increasingly important for wetland conservation and environmental planning. Deep learning for wetland identification is an emerging field that shows promise for advancing these efforts. Deep learning is unique to traditional machine learning techniques for its ability to consider the spatial context of object characteristics within a landscape training the algorithms, which limits their application for many environmental applications including wetland identification. Using four study sites across Virginia with field delineated wetlands, we provide insight into the potential for deep learning for wetland detection from limited, but typical, wetland delineation training data. Our proposed workflow performs a wetland semantic segmentation using DeepNets, a deep learning architecture for remote sensing data, and an input dataset consisting of high-resolution topographic indices and the Normalized Difference Vegetation Index. Results show that models trained and evaluated for a single site were able to achieve high accuracy (up to 91% recall and 56% precision) and similar accuracy can be obtained for models trained across multiple sites (up to 91% recall and 57% precision). Through this analysis we found that, across all sites, input data configurations taking advantage of hydrologic properties derived from elevation data consistently outperformed models using the elevation data directly, showing the benefit of physically-informed inputs in deep learning training for wetland identification. By refining the wetland identification workflow presented in this paper and collecting additional training data across landscapes, there is potential for deep learning algorithms to support a range wetland conservation efforts.

自动化和准确的湿地识别算法对于湿地保护和环境规划越来越重要。用于湿地识别的深度学习是一个新兴领域，显示出推动这些努力的希望。深度学习是传统机器学习技术所独有的，因为它能够在训练算法的景观中考虑对象特征的空间上下文，这限制了它们在包括湿地识别在内的许多环境应用中的应用。我们使用弗吉尼亚州的四个研究地点以及田间划定的湿地，从有限但典型的湿地划定训练数据中深入了解潜在的湿地探测深度学习潜力。我们建议的工作流程使用DeepNets执行湿地语义分割，一种用于遥感数据的深度学习架构，以及一个由高分辨率地形指数和归一化植被指数组成的输入数据集。结果表明，针对单个站点进行训练和评估的模型能够实现较高的准确性（高达91％的召回率和56％的精度），并且对于跨多个站点进行训练的模型可以获得类似的准确性（高达91％的召回率和57％的精度） ）。通过此分析，我们发现，在所有站点上，利用从高程数据得出的水文特性优势的输入数据配置始终优于直接使用高程数据的模型，这表明在深度学习训练中以信息为基础的输入对湿地识别的好处。