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).
@article{arxiv.1708.03417,
title = {GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery},
author = {Seungkyun Hong and Seongchan Kim and Minsu Joh and Sa-kwang Song},
journal= {arXiv preprint arXiv:1708.03417},
year = {2017}
}