用深度学习从卫星图自动绘制全球水道，里程超5400万的三倍。
Mapping waterways worldwide with deep learning
- 基于哨兵2号影像和地形数据，训练模型自动识别水道。
- 新增1.24亿公里水道，使全球已测绘水道总量超1.78亿公里。
- 适用于地球系统建模与灾害响应，适合地理信息研究者使用。
水道塑造地球系统过程与人类社会，更清晰地理解其分布可支持地球系统模拟、人类发展及灾害应对等多种应用。以往全球水道测绘多依赖大量建模与专家经验，成本高且难以重复，尤其在经济较落后地区仍存在诸多空白。本文提出一种计算机视觉模型，利用10米分辨率的哨兵2号卫星影像与30米分辨率的GLO-30 Copernicus数字高程模型，基于美国高精度水道数据进行训练，结合矢量化流程实现全球水道自动绘制。为便于下游应用与建模，新数据以另一数据集TDX-Hydro所含流域和水道为基础框架进行整合。最终，共为已有5400万公里水道的TDX-Hydro数据集新增1.24亿公里水道，使全球已测绘水道总长度超过1.78亿公里，实现总量三倍以上增长。
原文摘要 · Abstract (English)
Waterways shape earth system processes and human societies, and a better understanding of their distribution can assist in a range of applications from earth system modeling to human development and disaster response. Most efforts to date to map the world's waterways have required extensive modeling and contextual expert input, and are costly to repeat. Many gaps remain, particularly in geographies with lower economic development. Here we present a computer vision model that can draw waterways based on 10m Sentinel-2 satellite imagery and the 30m GLO-30 Copernicus digital elevation model, trained using high fidelity waterways data from the United States. We couple this model with a vectorization process to map waterways worldwide. For widespread utility and downstream modelling efforts, we scaffold this new data on the backbone of existing mapped basins and waterways from another dataset, TDX-Hydro. In total, we add 124 million kilometers of waterways to the 54 million kilometers already in the TDX-Hydro dataset, more than tripling the extent of waterways mapped globally.
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