LAUSR

Obtaining information on the surface coverage of open-pit mining areas (OPMAs) is of great significance to ecological governance and restoration. The current methods to map the OPMAs face problems such as low mapping accuracy due to complex landscapes. In this article, we propose a hybrid open-pit mining mapping (OPMM) framework with Gaofen-2 (GF-2) high-spatial resolution satellite images (HSRSIs), using an improved U-Net neural network (U-Net+). By concatenating the previous layers with each subsequent layer to ensure that there is a maximum of feature maps of each layer in the network, the U-Net+ can reduce the loss of feature information and make the extraction capability of the network more powerful. Two independent OPMAs were selected as the study area for the OPMM. By taking advantage of GF-2 HSRSIs, a total of 111 open-pit mine sites (OPMSs) were mapped and each OPMS boundary was validated by field surveys. Then, these OPMSs were used as input to assess the accuracy of the OPMM results obtained by the U-Net+. By comparing our results with those provided by five state-of-the-art deep learning algorithms: Fully Convolutional Network (FCN), SegNet, U-Net, Residual U-Net (ResU-Net), and U-Net++, we conclude that the proposed framework outperformed these methods by more than 0.02% in Overall Accuracy, 0.06% in Kappa Coefficient, 0.03% in Mean Intersection over union, 8.36% in producer accuracy and 4.44% in user accuracy. Therefore, the proposed framework thus exhibits very promising applicability in the ecological restoration and governance of OPMAs.