Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model

Abstract

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 (14C) 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which no detailed mapping data on karez has been obtained before) and successfully detected some karez that had not been archived before.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.

An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform

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