# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 1 Introduction  
Open-pit mining operations have a significant impact on both the natural environment and human activities, due to the extensive land alteration and resource extraction involved [1; 2; 3; 4; 5]. The demand for raw materials from industries such as construction, manufacturing, and energy production drives the expansion of open-pit mining worldwide. Large quantities of soil, rock, and minerals are removed during the extraction process in open-pit mines, significantly altering the landscape [6]. The excavation, as well as the construction of waste rock piles and tailing ponds [7], results in the modification of topography and the displacement of natural features. Additionally, mining operations can result in soil erosion, air pollution from emissions and dust, and the release of hazardous materials into the local ecosystem. Implementing efficient monitoring strategies for spottingand tracking changes in these environments is essential for ensuring sustainable resource utilization and mitigating the negative effects of open-pit mining. Indeed, accurate and timely detection of changes allows mining companies and environmental regulators to intervene quickly, allocate resources efficiently, and plan appropriate mitigation measures. Monitoring changes in open-pit mines, in addition, extends beyond the scope of environmental impact investigations. It is also critical for the safety and well-being of workers and the surrounding community [8]. Detecting and monitoring changes related to possible hazards, such as ground instability or structural collapses, is critical for avoiding accidents and saving lives [9]. The change detection task in open-pit mines presents significant challenges compared to other common change detection applications such as urban planning, flood detection, crop growth monitoring, land cover use, and natural resource management. Several factors contribute to these challenges. First, mining area land use types are highly complex and heterogeneous. Mining sites frequently include varied features, such as excavated areas, trash piles, infrastructure, vegetation, and water bodies, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates detecting and distinguishing changes within open-pit mines. Secondly, open-pit mines are exposed to regular mining activities, which result in a variety of changes that further complicate the detection procedure [10]. These changes not only include the expansion or reduction of mining areas but also pseudo changes, e.g., equipment movements or humidity fluctuations. These areas present statistically reliable reflectivity changes, but these do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, and waste pile operations. Distinguishing these pseudo changes from the significant changes, which are crucial for monitoring mining operations and environmental impacts, poses a substantial challenge in open-pit mine change detection. By training a model on the complex and heterogeneous land use types that can effectively differentiate between significant changes and pseudo changes, we can enhance the accuracy and reliability of change detection in open-pit mines. Optical sensors have been utilized for monitoring changes within open-pit mines and assessing the environmental impact of mining activities. [PERSON] [11] conducted a study using high-resolution optical imagery to monitor land use and land cover changes near open-pit mines. They demonstrated the importance of remote sensing data in capturing dynamic changes in vegetation, water bodies, and infrastructure caused by mining activities. [PERSON] [10] proposed a Siamese multiscale change detection network (SMCDNet) with an encoder-decoder structure designed for automatic change detection in open-pit mines using high-resolution remote sensing images. [PERSON] [12] introduced a novel deep learning model called DA-UNet++ (deformable-attention-UNet++) and an object-based approach to achieve automatic change detection in open-pit mines.