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Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
2022
Remote Sensing
In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of the northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs. We first labeled the cyclone center by adapting an approach from Bonfanti et al. in 2017 and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. We then gave a framework of labeling and preprocessing the images in our dataset. Once the images and
doi:10.3390/rs14020254
fatcat:bcx33o6c5fa4he4ezcfnxflocy