GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery

Seungkyun Hong; Seongchan Kim; Minsu Joh; Sa-kwang Song

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arXiv · 1708.03417

GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery

Seungkyun Hong·Seongchan Kim·Minsu Joh·Sa-kwang Song

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Abstract

Advances in remote sensing technologies have made it possible to use high-resolution visual data for weather observation and forecasting tasks. We propose the use of multi-layer neural networks for understanding complex atmospheric dynamics based on multichannel satellite images. The capability of our model was evaluated by using a linear regression task for single typhoon coordinates prediction. A specific combination of models and different activation policies enabled us to obtain an interesting prediction result in the northeastern hemisphere (ENH).

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Seungkyun Hong, Seongchan Kim, Minsu Joh, Sa-kwang Song. 2017-08-11. GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery. https://arxiv.org/abs/1708.03417

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