## 1 Introduction  
For a long time, due to the relative scarcity of remote sensing data, intelligent classification and element extraction methods have remained immature, meaning that they cannot realize the intelligent application of remote sensing on a large scale and at a deep level. With the increase of remote sensing images, remote sensing interpretation has begun to play an important role in environmental monitoring [1], resource investigation [2, 3], urban planning [4], etc. As a subfield of artificial intelligence, machine learning is expected to help achieve accurate and efficient remote sensing image classification and is therefore considered a reliable method [5]. With the breakthroughs in deep learning (e.g., ResNet, attention mechanisms, transformers, etc. ), new opportunities have arisen for the industry application of intelligent interpretation of remote sensing. It is well known that the intelligent interpretation of land cover based on high-resolution remote sensing images, especially classification with accurate types and clear boundaries, is a worldwide challenge. Compared with artificial facilities, natural features are characterized by irregular geometry, complex structure, multiple size scales, and large texture differences, which make it difficult to realize high-precision classification and extraction [6, 7, 8, 9]. In recent years, deep learning [10, 11, 12] has been regarded as an important means to replace manual interpretation,which presents broad prospects for the automatic interpretation, analysis, and content under-standing of utility-level remote sensing. If automatic interpretation can be truly realized, combined with high-resolution remote sensing, the current status and spatial distribution of land cover at the patch level can be quickly and accurately obtained, which is of great significance for applications such as the detailed management of natural resources and ecological-environmental restoration. Intelligent interpretation of all kinds of land cover elements from remote sensing images at one time is undoubtedly the most ideal solution. If the conditions are ripe, all of the geographical elements in a region can be systematically and comprehensively classified and extracted, and the one-time processing can meet the practical application needs of various industries. However, current modeling methods are still immature, the computational power of computers is still insufficient, and the labeled samples are not rich enough. Therefore, the intelligent interpretation of all the elements of remote sensing images is still far from meeting the needs of refined practical applications. At the same time, with the deepening of the digital economy and the construction of digital China, the demand for industry applications is growing, and each industry is more concerned about its own management objects, which are often of a certain type or several types. For example, the Ministry of Water Resources is concerned about surface water, the Ministry of Agriculture is concerned about crop cultivation, the Ministry of Transportation is concerned about the construction of transportation facilities, and so on. Therefore, un-der the existing conditions, it is a more feasible program to classify the various types of elements and extract them separately. On the one hand, this can promote in-depth research on and rapid development of remote sensing intelligent interpretation in practical applications, and on the other hand, it can better and faster serve the application needs of industry fields. Open-pit mining is the process of removing the overburden from an ore body in order to obtain the desired minerals, and it can be used for mineral extraction such as sand and gravel mines, coal mines, and iron ore mines. This type of mining has the advantages of low technical requirements, low investment, and low cost, but it also causes the most serious damage to the environment, as in the case of open-pit mining of sand and gravel mines.