GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery
This work addresses weather forecasting for meteorologists, but it appears incremental as it applies existing neural network methods to a specific domain task.
The authors tackled typhoon eye tracking from remote sensing imagery using convolutional neural networks, achieving an interesting prediction result for typhoon coordinates in the northeastern hemisphere.
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).