Over the past decade, remote sensing data have improved in resolution and become more widely available, bringing new opportunities for its use in environmental science and conservation. One potential application is to identify and map instream infrastructure across the world, with important implications for fisheries, hydrology, flooding, and more. To date, databases of instream infrastructure focus on larger dams with reservoirs that are comparatively easy to detect with remotely sensed imagery. Despite their impact on freshwater ecosystems, smaller infrastructure is often overlooked. To overcome these challenges, we require more systematic approaches, such as the Global River Obstruction Database (GROD) presented here, to map instream infrastructure. We present a participatory approach to identify, map, and validate infrastructure and provide an initial data set for the contiguous United States (n = 4,197). We highlight the value of participatory methods that include the public and suggest ways they could be fused with machine learning for future applications.

中文翻译：

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参与式科学方法可扩展河内基础设施清单

在过去的十年中，遥感数据的分辨率有所提高，并且变得更加广泛，为在环境科学和保护中的使用带来了新的机遇。一种潜在的应用是在全球范围内识别和绘制河流基础设施，对渔业，水文学，洪水等产生重要影响。迄今为止，河内基础设施数据库的重点是带有水库的大型水坝，这些水坝相对容易通过遥感图像进行检测。尽管它们对淡水生态系统有影响，但较小的基础设施常常被忽视。为了克服这些挑战，我们需要更系统的方法，例如此处介绍的全球河流阻塞数据库（GROD），以绘制河流基础设施图。我们提供一种参与式方法来识别，绘制，n  = 4,197）。我们强调了包括公众在内的参与式方法的价值，并提出了将它们与机器学习相融合以供将来应用的方法。