Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

Jianjun Lyu,Ying Hu,Shuliang Ren,Yao Yao,Qingfeng Guan,Liufeng Tao,Dan Ding

doi:10.3390/rs13040743

Jianjun Lyu, Ying Hu + Show 5 more

Open Access

https://doi.org/10.3390/rs13040743

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Abstract

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset of tailings ponds available for relevant research. This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing images. Taking Tongling city as the study area, the proposed model can detect tailings pond locations with high accuracy and efficiency from a large HSR remote sensing image (precision = 99.6%, recall = 89.9%, mean average precision = 89.7%). An optimal random forest model and morphological processing were utilized to further extract accurate tailings pond margins from the target areas. The final map of the entire study area was obtained with high accuracy. Compared with the random forest algorithm, the total extraction time was reduced by nearly 99%. This study can be beneficial to mine monitoring and ecological environmental governance.

Highlights

A tailings pond is a mine production facility used to store tailings generated during processing metal resources [1]
The framework can be summarized by the following steps: (1) creating a tailings based on the characteristics of the tailings ponds in high spatial resolution (HSR) remote sensing images; (2) training pond dataset based on the characteristics of the tailings ponds in HSR remote sensing the YOLOv4 model by the tailings pond dataset to obtain the tailings pond target areas; images; (2) training the YOLOv4 model by the tailings pond dataset to obtain the tailings
Combined with the morphology of tailing ponds in HSR remote sensing images, the margins of tailing ponds detected by YOLOv4 were outlined to verify the extraction results

Summary

Introduction

A tailings pond is a mine production facility used to store tailings generated during processing metal resources [1]. The liquid contained in tailings ponds is poisonous, harmful or radioactive [2]. The tailings pond usually requires a large area to contain all the produced tailings [3]. Restricted by mineral resources, terrain and other factors, tailings ponds are mostly located in remote mountainous areas with relatively weak supervision [4]. The number of known tailings ponds in China in 2019 was 5189. Most of them were distributed in isolation and nearly 80% of tailing ponds are less than

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