## 1 Introduction
Sand and gravel resources are widely distributed in nature and are strategic resources that have an important impact on national defense security, economic security, and sustainable economic and social development. They are mainly used for the construction of roads and buildings. Sand and gravel resources are easy to survey and mine, and open-pit mining is the mainstay. The phenomena of unauthorized mining, over-exploitation, and transboundary mining have never been eradicated in China, which not only disrupts the order of the exploitation and utilization of mineral resources but also causes serious damage to the ecological environment. Therefore, the rapid implementation of the regulation of open-pit mining and illegal mining has become one of the important tasks of the natural resources management authorities. However, it is well known that the current remote sensing intelligent classification and extraction technology is not mature; the accuracy is not high enough, and the processing speed is not fast enough. Therefore, the accuracy, efficiency, and automation level of the large-scale remote sensing classification and extraction of open-pit mining areas are far from the requirements of practical applications. There is an urgent need to conduct continuous and in-depth research on intelligent extraction methods for open-pit mining areas based on machine learning in order to significantly improve the extraction accuracy and processing speed. This will provide technical support for the Ministry of Natural Resources to comprehensively grasp the current situation of open-pit mining in China and quickly implement dynamic monitoring, analysis, and evaluation. Due to the wide application of the spatial data of artificial facilities, relatively simple texture features, and relatively clear boundaries, there are relatively more studies on their automatic classification and extraction, and they are also more mature. These artificial facilities include houses and buildings [13; 14], roads [15; 16], stadiums [17; 18], and so on. However, it is difficult to intelligently classify and extract natural elements with high accuracy from high-resolution remote sensing images, especially when the elements are covered by or affected by vegetation, such as open-pit mining areas, which are characterized by complex internal textures and sticky boundaries. Adhesion manifests itself here in the form of neighboring open-pit mining areas that should be separated by vegetation, etc., sticking together to varying degrees at different locations, resulting in extremely elongated areas or nearly overlapping boundaries. The life of an open-pit mine has gone through a process that started from scratch, with the open-pit mining area expanding as production continues. For example, the open-pit mining area of the Fushun West Open-Pit Coal Mine approximates an east-west rectangle, measuring 6.6 km east-west and 2.2 km north-south [19]. The huge difference in scale also brings difficulties in extracting open-pit mining areas with high accuracy. Although the rapid development of image processing, pattern recognition, and computer vision has created conditions for the improvement of the level of image classification and feature extraction, their application in the extraction of open-pit mining areas is not mature enough. All these characteristics of open-pit mining areas put forward high requirements for deep learning methods, and the existing methods are not strong enough to support them, so in-depth research is needed. In order to optimize the remote sensing extraction of open-pit mining areas in complex environments, we propose a deep learning-based semantic segmentation model called Segmentation for Mine--SegMine--which consists of a Vision Transformer-based encoder and a lightweight attention mask decoder. Our innovations and main contributions can be summarized as follows:  
1.