A 30-year ocean front dataset from 1993 to 2023 for the Northwest Pacific Ocean based on deep learning

Ocean fronts are critical interfaces between different water masses and profoundly influence atmosphere–ocean interactions, weather systems, marine ecosystems, and climate regulation. Accurate and long-term observations of ocean fronts are essential for advancing studies in meteorology, oceanography, and climate science. However, no publicly available, long-term ocean front dataset currently exists, and the existing detection methods often rely on time-consuming manual labeling or traditional algorithms with limited accuracy in complex frontal regions. In this study, we release the first publicly available 30-year ocean front dataset (1993–2023) for the Northwest Pacific, which was generated by applying a deep learning framework (Mask R-CNN) to daily sea surface temperature (SST) fields with manually annotated samples for model training. The model was trained utilizing both L3 remote sensing satellite SST data and the GLORYS12V1 L4 reanalysis SST product. The dataset provides pixel-level frontal boundaries along with the associated attributes, including the position, intensity, and width, stored in NetCDF-4 format at a 1/12° spatial and daily temporal resolution. An accuracy evaluation shows that the mean average precision (mAP) exceeds 0.90, and compared with traditional gradient methods, the errors in front width and intensity are smaller. The dataset offers three main contributions: (1) It fills a critical gap by providing a standardized, long-term ocean front product; (2) it serves as a ready-to-use training resource for deep learning models that greatly reduces the need for manual labeling; and (3) it provides benchmark samples for validation and intercomparison of other ocean front detection products. This dataset supports robust investigations of the seasonal-to-interannual frontal variability and provides a valuable foundation for applications in meteorology, ecosystems management and climate change research. The ocean front dataset developed in this study is available at https://doi.org/10.5281/zenodo.16921277 (Niu, 2025a).