干旱区生态环境知识资源中心(Arid): Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia

Arid

DOI	10.1080/17538947.2024.2384631
	Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia
	Wang, Meng; Wang, Juanle
通讯作者	Wang, JL
来源期刊	INTERNATIONAL JOURNAL OF DIGITAL EARTH
ISSN	1753-8947
EISSN	1753-8955
出版年	2024
卷号	17 期号:1
英文摘要	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 their dynamic and obscure features. Moreover, the high cost of data annotation restricts the availability of comprehensively labelled datasets, which are essential for advanced remote sensing processing and natural road detection. This study dedicated to implement a semi-supervised deep learning method for dirty road extraction in southern Mongolia. A new thematic semantic segmentation dataset of natural roads was established firstly to address scarcity of annotation datasets this region. A semi-supervised UniMatch structure was designed consequently. Operating with high-resolution GaoFen-2 images, this approach minimises the need for extensive manual annotation, achieving an IOU of 73.51% and MIOU of 86.37%. This method significantly reduces labour and time costs associated with manual and fully supervised methods. These observations provide a valuable data source and methodology for addressing natural road expansion in arid regions. They can aid governments in evaluating transportation infrastructure in remote areas, and analysing dirty road traffic impact on environment.
英文关键词	Deep learning semi-supervised semantic segmentation off-road natural road Mongolian plateau
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001280580100001
WOS关键词	SATELLITE IMAGES ; RESOLUTION ; FEATURES
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404223
推荐引用方式 GB/T 7714	Wang, Meng,Wang, Juanle. Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia[J],2024,17(1).
APA	Wang, Meng,&Wang, Juanle.(2024).Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1).
MLA	Wang, Meng,et al."Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024).