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

2017/08/11 by Hong, Seungkyun, Kim, Seongchan, Joh, Minsu +1
#Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.2.6 #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.1708.03417

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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