High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932.

In the context of global sustainable development goals and heightened environmental awareness, the imperative to evaluate the eco-efficiency of mining becomes paramount. However, the escalated mining activities result in the generation of wastewater, exhaust gas, and solid waste, posing a threat to the environment. This study endeavors to assess the mining eco-efficiency across Chinese provinces by introducing a pioneering assessment index system and framework. The framework incorporates the non-desired output SBM-DEA model and the Malmquist-Luenberger total factor productivity index model. To validate the reliability of the model, it is applied to 27 provinces in China. The findings unveil significant insights: (1) The mining eco-efficiency of Chinese provinces exhibits an overall positive trend but displays notable spatial variations; (2) East China demonstrates superior technical progress and overall technical efficiency, while North, Northeast, and Northwest China lag in technical progress but excel in overall technical efficiency. Notably, non-desired outputs exert an influence on China’s level of green mining development, particularly in Northeast and Central China. The study recommends that enterprises shoulder the responsibility of environmental management and mine restoration, enhance their capacity for innovation in green technology, and expedite the construction of green mines to augment mining eco-efficiency. These results furnish valuable perspectives and pertinent information for decision-making related to green mining development, energy structure transformation, and the implementation of large-scale mining projects.