A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll-a Data based on Deep Learning
This paper presents an observational dataset on submesoscale eddies, which obtains from high-resolution chlorophyll-a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7-X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic 15 eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to submesoscale eddies of study. The dataset is available at https://doi.org/10.5281/zenodo.7694115.