Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia

IF 3.7 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL International Journal of Digital Earth Pub Date : 2024-07-30 DOI:10.1080/17538947.2024.2384631
Meng Wang, Juanle Wang
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Abstract

The uncontrolled proliferation of natural roads in arid regions has exacerbated regional land degradation and desertification, presenting substantial challenges to their accurate mapping owing to t...
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利用半监督深度学习方法从 GF-2 图像中提取脏路,用于蒙古南部干旱和半干旱地区
干旱地区天然道路无节制地增加,加剧了区域土地退化和荒漠化,对其精确测绘造成了巨大挑战,原因是......
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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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