论文标题
街道到云：通过众包和语义细分改善洪水图
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
论文作者
论文摘要
为了解决由气候险种区域中洪水造成的越来越多的破坏，我们向云提议，这是一条机器学习管道，将众包地面真相数据纳入洪水卫星图像的细分。我们建议这种方法作为解决劳动密集型任务的解决方案，即生成高质量的手工标记培训数据，并在我们的模型中展示了不同合理的众包方法的成功和失败。街道到云利用社区报告和机器学习，以产生新颖的，近乎真实的时间见解，以实现紧急响应的洪水程度。
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods. We propose this approach as a solution to the labor-intensive task of generating high-quality, hand-labeled training data, and demonstrate successes and failures of different plausible crowdsourcing approaches in our model. Street to Cloud leverages community reporting and machine learning to generate novel, near-real time insights into the extent of floods to be used for emergency response.
