Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, training a wildfire classifier on satellite imagery requires curating a massive and diverse dataset, which is an expensive and time-consuming process that can span from weeks to months. Searching for relevant examples in over 40 petabytes of unlabelled data requires researchers to manually hunt for such images, much like finding a needle in a haystack. We present a no-code end-to-end pipeline, Curator, which dramatically minimizes the time taken to curate an exhaustive labeled dataset. Curator is able to search massive amounts of unlabelled data by combining self-supervision, scalable nearest neighbor search, and active learning to learn and differentiate image representations. The pipeline can also be readily applied to solve problems across different domains. Overall, the pipeline makes it practical for researchers to go from just one reference image to a comprehensive dataset in a diminutive span of time.

本研究提出了一个名为Curator的自动化数据集筛选工具，利用自我监督、可扩缩的最近邻检索和主动学习等技术以及机器学习算法自主对未标记数据进行搜索，可大大缩短筛选数据集的时间。研究展示了Curator在卫星图像野火分类方面的应用，这也说明了Curator不仅适用于地球科学领域，且可以轻松应用于其他领域的问题解决。

Curator：使用自监督学习创建大规模策展标注数据集