Deep Learning-Based Calculation Method for the Dry Beach Length in Tailing Ponds Using Satellite Images

• Published in: Applied sciences Vol. 14; no. 17; p. 7560
• Main Authors: Duan, Zhijie, Tian, Yu, Li, Quanming, Liu, Guangyu, Cui, Xuan, Zhang, Shumao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Artificial intelligence; Beaches; Cameras; Dams; Deep learning; dry beach length; Failure; Heavy metals; Lasers; Measurement techniques; Ponds; satellite image; Software; tailings pond; Water area; YOLOv5; China
• ISSN: 2076-3417
• DOI: 10.3390/app14177560

The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper’s case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.