干旱区生态环境知识资源中心(Arid): Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model

Arid

DOI	10.3390/rs14143318
	Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model
	Li, Qian; Guo, Huadong; Luo, Lei; Wang, Xinyuan
通讯作者	Luo, L
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:14
英文摘要	As a large-scale irrigation and water conservancy project in ancient times, karez are common in Central Asia and arid regions with a history of thousands of years. Turpan, which is located in the Xinjiang Uyghur Autonomous Region, has the most extensive and concentrated distribution of karez shafts in China. There are tens of thousands of shafts, some of which are in use and are living cultural heritage. According to radiocarbon (C-14) dating, some karezs are over 600 years old. The karez is of great significance to the research on geology, hydrology, oasis, climate change, and development history of karez in Turpan. With the development of the population, arable land, industrialization, and urbanization, karez systems are facing the risk of abandonment. Detailed karez distribution mapping or dynamic monitoring data are important for their management or analysis; although there are related methods, due to Turpan's large desert and Gobi environments, field surveys are time- and energy-consuming, and some areas are difficult to access. Precise shaft locations and distribution maps are scarce and often lack georeferencing. The distribution and preservation of karez have not yet been fully grasped. In this study, we evaluated the effectiveness of You Only Look Once version 5 (YOLOv5) in automatically detecting karez in high-resolution images of the Turpan region. We propose post-processing steps to reduce the false karez identified by YOLOv5. Our results demonstrate the feasibility of using YOLOv5 and post-processing techniques to detect karez automatically, and the detected results are sufficient to capture the linear alignment of karez. Target detection based on YOLOv5 and post-processing can greatly improve automatic shaft identification and is therefore useful for the fine mapping of karez. We also applied this method in Shanshan County (for which no detailed mapping data on karez has been obtained before) and successfully detected some karez that had not been archived before. The number of shafts in Turpan is 82,493. Through DBSCAN clustering, it was identified which karez line belonged to which shaft; the number of sections of karez that have been used is 5057, which have a total length of 2387.2 km. The karez line obtained was overlaid with the crop-land data, and the positional relationship between the karez line and the crop land was analyzed. The cultivated area is basically surrounded by karez. Our method can potentially be applied to construct an inventory for all karez shafts globally.
英文关键词	karez shaft object detection Turpan Basin YOLOv5
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:000831689800001
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394172
推荐引用方式 GB/T 7714	Li, Qian,Guo, Huadong,Luo, Lei,et al. Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model[J],2022,14(14).
APA	Li, Qian,Guo, Huadong,Luo, Lei,&Wang, Xinyuan.(2022).Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model.REMOTE SENSING,14(14).
MLA	Li, Qian,et al."Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model".REMOTE SENSING 14.14(2022).