Tropical cyclone intensity classification and estimation using infrared satellite images with deep learning
CJ Zhang, XJ Wang, LM Ma… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
A novel tropical cyclone (TC) intensity classification and estimation model (TCICENet) is
proposed using infrared geostationary satellite images from the northwest Pacific Ocean …
proposed using infrared geostationary satellite images from the northwest Pacific Ocean …
Transfer learning-based automatic hurricane damage detection using satellite images
After the occurrence of a hurricane, assessing damage is extremely important for the
emergency managers so that relief aid could be provided to afflicted people. One method of …
emergency managers so that relief aid could be provided to afflicted people. One method of …
Digital typhoon: Long-term satellite image dataset for the spatio-temporal modeling of tropical cyclones
This paper presents the official release of the Digital Typhoon dataset, the longest typhoon
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
Domain knowledge integration into deep learning for typhoon intensity classification
M Higa, S Tanahara, Y Adachi, N Ishiki, S Nakama… - Scientific reports, 2021 - nature.com
In this report, we propose a deep learning technique for high-accuracy estimation of the
intensity class of a typhoon from a single satellite image, by incorporating meteorological …
intensity class of a typhoon from a single satellite image, by incorporating meteorological …
A review on natural disaster detection in social media and satellite imagery using machine learning and deep learning
A disaster is a devastating incident that causes a serious disruption of the functions of a
community. It leads to loss of human life and environmental and financial losses. Natural …
community. It leads to loss of human life and environmental and financial losses. Natural …
Convolutional neural network based hurricane damage detection using satellite images
Hurricanes are tropical storms that cause immense damage to human life and property.
Rapid assessment of damage caused by hurricanes is extremely important for the first …
Rapid assessment of damage caused by hurricanes is extremely important for the first …
DMANet_KF: Tropical cyclone intensity estimation based on deep learning and Kalman filter from multispectral infrared images
It is very crucial to identify the intensity of tropical cyclone (TC) accurately. In this article, a
novel TC intensity estimation method is proposed to estimate the TC intensity from …
novel TC intensity estimation method is proposed to estimate the TC intensity from …
Hurricane damage detection using machine learning and deep learning techniques: a review
Hurricane is one of the most disastrous natural disasters causing immense harm to the
ecosystem and economic system worldwide. It is also known as a tropical cyclone. Heavy …
ecosystem and economic system worldwide. It is also known as a tropical cyclone. Heavy …
An efficient framework for solving forward and inverse problems of nonlinear partial differential equations via enhanced physics-informed neural network based on …
Y Guo, X Cao, J Song, H Leng, K Peng - Physics of Fluids, 2023 - pubs.aip.org
In recent years, the advancement of deep learning has led to the utilization of related
technologies to enhance the efficiency and accuracy of scientific computing. Physics …
technologies to enhance the efficiency and accuracy of scientific computing. Physics …
A neural network with spatiotemporal encoding module for tropical cyclone intensity estimation from infrared satellite image
Accurate and instant estimation of tropical cyclone (TC) intensity is crucial for emergency
decision making. Although deep neural networks and satellite images have been …
decision making. Although deep neural networks and satellite images have been …