用深度学习补全非洲未测绘水道，助力农村基建精准规划
Deep learning waterways for rural infrastructure development
- 基于卫星影像与高程数据训练WaterNet模型识别水道
- 新地图覆盖93%社区基建需求，远超开源与主流数据
- 适合关注乡村发展与人道主义地图的决策者使用
地球上大量水道仍未被测绘，尤其集中在低收入和中等收入国家。本文构建了计算机视觉模型WaterNet，利用美国的高分辨率卫星影像和数字高程模型进行训练，并将其部署于非洲新环境。生成结果揭示了此前未被记录的水道结构。在评估其对农村桥梁建设需求的覆盖能力时，针对学校、医疗设施和农业市场的访问需求，新地图平均覆盖率达93%（各国范围88%-96%），而开放街图和最新的TDX-Hydro数据分别仅覆盖36%（5%-72%）和62%（37%-85%）。由于该方法依赖公开且可操作的数据获取，为在传统制图失败地区捕捉人道需求并规划社会发展提供了可行路径。模型在识别现有数据遗漏的社区需求方面表现优异，显示出显著价值，有助于提升农村基础设施建设效率与援助目标精准度。
原文摘要 · Abstract (English)
Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African continent. Our outputs provide detail of waterways structures hereto unmapped. When assessed against community needs requests for rural bridge building related to access to schools, health care facilities and agricultural markets, we find these newly generated waterways capture on average 93% (country range: 88-96%) of these requests whereas Open Street Map, and the state of the art data from TDX-Hydro, capture only 36% (5-72%) and 62% (37%-85%), respectively. Because these new machine learning enabled maps are built on public and operational data acquisition this approach offers promise for capturing humanitarian needs and planning for social development in places where cartographic efforts have so far failed to deliver. The improved performance in identifying community needs missed by existing data suggests significant value for rural infrastructure development and better targeting of development interventions.
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