Surface subsidence caused by coal mining has a great impact on the geological and ecological environments and causes damage to houses, roads, and industrial buildings. In order to understand the subsidence pattern in the mountainous mining regions, three mining faces of the Zhangjiamao mining area in the north of Shaanxi province, northwestern China are taken as case study. Firstly, the small baseline subset (SBAS) technology is used to process 12 images obtained in the mining area to investigate the subsidence data from December 2019 to April 2020. The boundary of surface deformation of the mining area interpreted by the SBAS-InSAR technology is inconsistent with the theoretical boundary suggested by coal mine subsidence theories. Especially, there are some areas in which the real subsidence are larger than estimated area. This discrepancy must be corrected as steep slopes near the theoretical boundary may increase the likelihood of landslides. Our research indicates that: (1) The accumulated displacement and the maximum deformation rate reached −120.759 mm and −270.012 mm/yr in the study area, and the subsidence boundary of the three mining faces is revealed; (2) the combination of the predicted boundary and slope stability analysis can effectively identify the landslide region at the edge of subsidence boundary; (3) the field surveys have proved the effectiveness of this method. The mining area subsidence revealed by our research helps to further understand the impact of land subsidence caused by mining in the mountainous areas and provides a practical method to predict subsidence boundaries and the likelihood for landslides.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

The inadequate ventilation and complex environments in underground coal mines lead to a high concentration of dust particles. As a result, the health of the miners and the accuracy of laser rangefinder measurements are endangered. It is crucial to enhance the laser rangefinder’s efficiency to mitigate health risks and reduce labor intensity. In this study, we propose a laser power attenuation model and a ranging correction model to address the issues of laser power attenuation and inaccurate ranging in coal dust environments. The proposed models are based on theoretical analysis and practical experiments, and both are dependent on the dust particle size (<250 μm) and mass concentration. Firstly, we assessed the factors that caused laser power attenuation and demonstrated that our proposed model could accurately predict them (maximum residual of 0.06). Secondly, we obtained the connection between the attenuation coefficient and dust concentration by applying the Lambert–Beer law. Lastly, we established the ranging correction model by collecting laser wavelength information. The outcomes show that the root mean square error of the corrected values ranges between 0.27 and 0.47 mm. To summarize, our suggested model and correction technique can efficiently enhance the precision of laser rangefinder measurements, thus improving underground work in coal mines.

Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast deformation rates, hindering its application for potential risk evaluation over the mining areas. In this study, we proposed a three-dimensional and full parameter inversion (TDFPI) model to derive the large-gradient subsidence and then investigate its application for building damage assessment over coal mining areas. By taking the Guotun coal mine as the case study, the TDFPI model was demonstrated to have effectively predicted the large-gradient deformation of the mining areas and successfully evaluated the house damage in Chelou village, which agrees well with our field investigations. Specifically, the predicted subsidence results were validated with high fitting accuracy against field measurements, with RMSE of 0.083 m and 0.102 m, respectively, on observation line A and line F. In addition, the classified damage levels are highly consistent with in situ field surveys for the house cracks in Chelou village, presenting its practicality and effectiveness for building damage evaluation, and thus can provide a useful tool for potential risk assessment and prevention over the mining areas.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of feature descriptors. However, the existing feature descriptors confront several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposed a feature descriptor for image stitching, denoted as Lightweight Multi-Feature Descriptors (LMFD). By extensively extracting gradients, means, and global information surrounding the feature points, the feature descriptors are generated through various combinations to enhance the image stitching. This empowers the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0 and 1, which not only facilitates more efficient hardware deployment but also enhances computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of the LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrated that the LMFD outperformed state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of the LMFD and substantiates its potential for practical applications in various domains.