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

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).

By Hong, Seungkyun, Kim, Seongchan, Joh, Minsu, Song, Sa-kwangResearch paper

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Authors: Hong, Seungkyun, Kim, Seongchan, Joh, Minsu, Song, Sa-kwang
Research area: Artificial intelligence
Research topics: Neural and Evolutionary Computing (cs.NE), Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.6
Publication date: 2017
DOI: 10.48550/arxiv.1708.03417
Upstream last modified: Mar 3, 2022
License: arXiv.org perpetual, non-exclusive license
Last record update: Nov 4, 2026