Deep learning for Digital Typhoon: Exploring a typhoon satellite image dataset using deep learning

Supervised by Prof. Kitamoto Asanobu, Prof. Xavier Giró-i-Nieto, Prof. Josephine Sullivan

Efficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several different approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset “Digital Typhoon”. We focus on intensity measurement and categorisation of different natural phenomena. Firstly, we build a classifier to differentiate natural tropical cyclones and extratropical cyclones and, secondly, we implement a regression model to estimate the centre pressure value of a typhoon. In addition, we also explore cleaning methodologies to ensure that the data used is reliable. The results obtained show that deep learning techniques can be effective under certain circumstances, providing reliable classification/regression models and feature extractors. More research to draw more conclusions and validate the obtained results is expected in the future.