Ocean Data Quality Assessment through Outlier Detection-enhanced Active
  Learning
Na Li Yiyang Qi Ruyue Xin Zhiming Zhao
Ocean and climate research benefits from global ocean observation initiatives
such as Argo, GLOSS, and EMSO. The Argo network, dedicated to ocean profiling,
generates a vast volume of observatory data. However, data quality issues from
sensor malfunctions and transmission errors necessitate stringent quality
assessment. Existing methods, including machine learning, fall short due to
limited labeled data and imbalanced datasets. To address these challenges, we
propose an ODEAL framework for ocean data quality assessment, employing AL to
reduce human experts' workload in the quality assessment workflow and
leveraging outlier detection algorithms for effective model initialization. We
also conduct extensive experiments on five large-scale realistic Argo datasets
to gain insights into our proposed method, including the effectiveness of AL
query strategies and the initial set construction approach. The results suggest
that our framework enhances quality assessment efficiency by up to 465.5% with
the uncertainty-based query strategy compared to random sampling and minimizes
overall annotation costs by up to 76.9% using the initial set built with
outlier detectors.