中国河南省2016–2021年尾矿库目标检测数据集

Abstract

Tailings ponds are essential infrastructures in mining operations, but they also pose a significant risk source as potential sources of manmade debris flow with high potential energy. The object detection of tailings pond in remote sensing imagery is to accurately recognize and pinpoint the locations of them on the images. Compared with traditional methods, tailings pond detection in remote sensing imagery with the aid of deep learning has seen substantial improvements in accuracy, stability, and efficiency especially when trained on high quality training dataset. Based on years of China high-resolution satellite remote sensing images, through data processing, manual interpretation and annotation, image slicing and other steps, we have constructed a dataset of object detection of tailings ponds in Henan Province, China, available for public access. This dataset has the following characteristics: (1) the domestic high-resolution dataset comprising 1,183 slices and 1,728 object instances; (2) multi temporal dataset containing a total of four different years of sample data in 2016, 2018, 2020 and 2021; (3) objects labeled with oriented bounding box, with less image background interference. The dataset can be used for the technical research on the development of tailings pond detection models with deep learning, as well as for the automatic and intelligent detection of tailings pond, It is of great significance for promoting the development of automatic extraction technology and safety supervision of tailings ponds.