Dirty road extraction from GF-2 images by semi-supervised deep learning method for arid and semiarid regions of southern Mongolia
文献类型：期刊论文
| 作者 | Wang, Meng1,3; Wang, Juanle1,2,3 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2024-12-31 |
| 卷号 | 17期号:1页码:2384631 |
| 关键词 | Deep learning semi-supervised semantic segmentation off-road natural road Mongolian plateau |
| DOI | 10.1080/17538947.2024.2384631 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 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. |
| WOS关键词 | SATELLITE IMAGES ; RESOLUTION ; FEATURES |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| WOS记录号 | WOS:001280580100001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206863]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China |
| 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]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):2384631. |
| 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),2384631. |
| 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):2384631. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。