Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

Qingting Li,Linlin Lu,Huadong Guo,Bing Zhang,Kaixuan Lu,Baipeng Li,Zhengchao Chen

doi:10.3390/rs12162626

Qingting Li, Linlin Lu + Show 5 more

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https://doi.org/10.3390/rs12162626

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Abstract

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

Highlights

Mining activities and infrastructure expansion cause great changes to the landscape and lead to social, economic, and environmental impacts in the surrounding areas [1,2]
To correct and eliminate the geometric distortion and increase the geometric accuracy of the original image, orthorectification mainly based on rational polynomial coefficients (RPC) file and digital elevation model (DEM) data was performed to the calibrated images
To meet the requirements of fast and accurate extraction of tailings ponds, a target detection method based on Single Shot Multibox Detector deep learning was developed in this study

Summary

Introduction

Mining activities and infrastructure expansion cause great changes to the landscape and lead to social, economic, and environmental impacts in the surrounding areas [1,2]. Tailings storage facilities and waste rock dumps account for a large proportion of the area of mining sites [4]. Historic occurrences of flow failures of mine waste dumps and tailings dams from 1928 to 2000 have resulted in deaths and damage to the environment and infrastructures [5]. Failures of mine tailings dams and the resulting mudflows have caused the loss of almost 2000 lives over the past century [1]. These dam failures often cause enormous quantities of tailings to be released into river catchments and lead to tragic consequences for populations living downstream. And accurate monitoring and mapping of mine tailings dams is crucial to the improvement of management practices and to preventing the occurrence of disasters caused by dam failures

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