AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena
IF 3.7
1区 地球科学
Q1 GEOGRAPHY, PHYSICAL
引用次数: 0
Abstract
This study proposes an innovative deep learning-aided approach based on generative adversarial networks named AttGAN, which is specialized for solving the spatio-temporal super-resolution problem o...AttGAN：用于海洋现象时空超分辨率的注意门控生成对抗网络
本研究提出了一种基于生成式对抗网络的创新型深度学习辅助方法--AttGAN，该方法专门用于求解...
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来源期刊
CiteScore
6.50
自引率
3.90%
发文量
88
审稿时长
3 months
期刊介绍：
The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that:
Progress visions for Digital Earth frameworks, policies, and standards;
Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery;
Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared;
Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level;
Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning;
Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and
Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
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