Benches, important surface features in open-pit coal mines, can reflect the production status in the mines. Extracting information about benches from remote sensing images can provide a significant basis for production monitoring in coal mines, as well as ecological protection and restoration. This study established the BenchSegNet deep learning model for extracting information on benches in open-pit coal mines from Sentinel-2 images. The results indicate that the BenchSegNet model inherited the strong generalization capability of SegFormer and the powerful detail extraction ability of U-Net, achieving an accuracy of 97.69%. Compared to the SegFormer model, the BenchSegNet model demonstrated increases of 6.19 percentage points, 4.09 percentage points, and 5.06 percentage points in precision, recall, and F1 score, respectively. Compared to two traditional convolutional neural network models, i.e., U-Net and ASPP-UNet, the BenchSegNet model exhibited increases of nearly 10 percentage points in the three metrics. In addition, compared to two traditional machine learning algorithms, i.e., random forest and support vector machine, the BenchSegNet model showed increases of approximately 15 percentage points in the three metrics. The comparisons verify that the BenchSegNet deep learning model delivers high accuracy. Given that the Sentinel-2 satellite is characterized by global coverage, short revisit time, and high spatial resolution, the combination of Sentinel-2 images and the BenchSegNet model can effectively monitor the change process of benches in open-pit coal mines.
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LI Kaixuan, LIU Junwei, WANG Zhibo, JIANG Wenlong, CAI Hanlin, LEI Shaogang, YANG Yongjun. Extracting information on benches in open-pit coal mines based on Sentinel-2 images and the BenchSegNet model. Remote Sensing for Natural Resources, 2025, 37(6): 148-155.
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2025, Vol. 37 
), 刘俊伟, 王智博, 蒋文泷, 蔡汉林, 雷少刚, 杨永均(
