Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning (ML). High-resolution maps of inland surface water bodies are important for water supply and natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems. In this paper, we propose an automatic surface water mapping workflow by training a deep residual neural network (ResNet) based on OpenStreetMap (OSM) data and Sentinel-2 multispectral data, where the Simple Non-Iterative Clustering (SNIC) superpixel algorithm was employed for generating object-based training samples. As a case study, we produced an open surface water layer for Germany using a national ResNet model at a 10 m spatial resolution, which was then harmonized with OSM data for final surface water products. Moreover, we evaluated the mapping accuracy of our open water products via conducting expert validation campaigns, and comparing to existing water products, namely the WasserBLIcK and Global Surface Water Layer (GSWL). Using 4,600 validation samples in Germany, the proposed model (ResNet+SNIC) achieved an overall accuracy of 86.32% and competitive detection rates over the WasserBLIcK (87.47%) and GSWL (98.61%). This study provides comprehensive insights into how to best explore the synergy of VGI and ML of EO data in a large-scale surface water mapping task.

大规模制图活动可以受益于地球观测 (EO) 数据可用性的大幅增加，尤其是在与使用机器学习 (ML) 的志愿地理信息 (VGI) 相结合时。内陆地表水体的高分辨率地图对于供水和减轻自然灾害以及监测、管理和保护景观和生态系统非常重要。在本文中，我们通过训练基于 OpenStreetMap (OSM) 数据和 Sentinel-2 多光谱数据的深度残差神经网络 (ResNet) 提出了一种自动地表水映射工作流程，其中采用了简单非迭代聚类 (SNIC) 超像素算法用于生成基于对象的训练样本。作为案例研究，我们使用 10 m 空间分辨率的国家 ResNet 模型为德国制作了一个开放的地表水层，然后将其与最终地表水产品的 OSM 数据进行协调。此外，我们通过进行专家验证活动并与现有水产品（即 WasserBLIcK 和全球地表水层 (GSWL)）进行比较，评估了我们开放水域产品的绘图准确性。使用德国的 4,600 个验证样本，所提出的模型 (ResNet+SNIC) 实现了 86.32% 的整体准确率和比 WasserBLIcK (87.47%) 和 GSWL (98.61%) 具有竞争力的检测率。本研究为如何在大规模地表水测绘任务中最好地探索 EO 数据的 VGI 和 ML 的协同作用提供了全面的见解。并与现有的水产品，即 WasserBLIcK 和全球地表水层 (GSWL) 进行比较。使用德国的 4,600 个验证样本，所提出的模型 (ResNet+SNIC) 实现了 86.32% 的整体准确率和比 WasserBLIcK (87.47%) 和 GSWL (98.61%) 具有竞争力的检测率。本研究为如何在大规模地表水测绘任务中最好地探索 EO 数据的 VGI 和 ML 的协同作用提供了全面的见解。并与现有的水产品，即 WasserBLIcK 和全球地表水层 (GSWL) 进行比较。使用德国的 4,600 个验证样本，所提出的模型 (ResNet+SNIC) 实现了 86.32% 的整体准确率和比 WasserBLIcK (87.47%) 和 GSWL (98.61%) 具有竞争力的检测率。本研究为如何在大规模地表水测绘任务中最好地探索 EO 数据的 VGI 和 ML 的协同作用提供了全面的见解。