A Deep Learning Framework for the Detection of Tropical Cyclones from
Satellite Images
Abstract
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.