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A Deep Learning Framework for the Detection of Tropical Cyclones from Satellite Images
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  • Aravind Nair ,
  • K S S Sai Srujan ,
  • Sayali Kulkarni ,
  • Kshitij Alwadhi ,
  • Navya Jain ,
  • Hariprasad Kodamana ,
  • Sandeep Sukumaran ,
  • Viju John
Aravind Nair
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K S S Sai Srujan
IIT Delhi

Corresponding Author:[email protected]

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Sayali Kulkarni
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Kshitij Alwadhi
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Navya Jain
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Hariprasad Kodamana
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Sandeep Sukumaran
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Viju John
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Tropical cyclones (TCs) are the most destructive weather systems that form over the tropical oceans, with 90 storms forming globally every year. The timely detection and tracking of TCs are important for advanced warning to the affected regions. As these storms form over the open oceans far from the continents, remote sensing plays a crucial role in detecting them. Here we present an automated TC detection from satellite images based on a novel deep learning technique. In this study, we propose a multi-staged deep learning framework for the detection of TCs, including, (i) a detector - Mask Region-Convolutional Neural Network (R-CNN), (ii) a wind speed filter, and (iii) a classifier - CNN. The hyperparameters of the entire pipeline is optimized to showcase the best performance using Bayesian optimization. Results indicate that the proposed approach yields high precision (97.10%), specificity (97.59%), and accuracy (86.55%) for test images.
2022Published in IEEE Geoscience and Remote Sensing Letters volume 19 on pages 1-5. 10.1109/LGRS.2021.3131638