Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV) imagery, combined with object-oriented methods, optimal segmentation algorithms, and machine learning algorithms, to develop an efficient and practical method for classifying land use in open-pit mines. First, six land use categories were identified: stope, restoration area, building, vegetation area, arterial road, and waters. To achieve optimal scale segmentation, an image segmentation quality evaluation index is developed, emphasizing both high intra-object homogeneity and high inter-object heterogeneity. Second, spectral, index, texture, and spatial features are identified through out-of-bag (OOB) error of random forest and recursive feature elimination (RFE) to create an optimal multi-feature fusion combination. Finally, the classification of open-pit mines was executed by leveraging the optimal feature combination, employing the random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers in a comparative analysis. The experimental results indicated that classification of appropriate scale image segmentation can extract more accurate land use information. Feature selection effectively reduces model redundancy and improves classification accuracy, with spectral features having the most significant effect. The RF algorithm outperformed SVM and KNN, demonstrating superior handling of high-dimensional feature combinations. It achieves the highest overall accuracy (OA) of 90.77%, with the lowest misclassification and omission errors and the highest classification accuracy. The disaggregated data facilitate effective monitoring of ecological changes in open-pit mining areas, support the development of mining plans, and help predict the quality and heterogeneity of raw clay in some areas.

Standard 3D Gaussian Splatting (3DGS) relies on multi-view images, creating a barrier for museum artifacts archived only as colored point clouds without corresponding registered images. We propose a 3DGS paradigm that operates directly on colored point clouds, enabling high-fidelity reconstruction independent of external image sets. Our approach is guided by dual priors. First, a feature-aware sampling algorithm constructs a precise geometric prior with absolute scale from the input point cloud, serving as the geometric foundation. Second, an ideal visual prior provides supervision by generating synthetic views from the point cloud, which are then refined using an enhancement chain that includes anti-aliasing and super-resolution. Experiments on public and self-built datasets validate our method’s superior rendering quality, achieving a significant PSNR improvement of 3.25 dB over the baseline method on our self-built dataset. The proposed framework thus offers an innovative and robust pipeline for effectively leveraging vast archives of existing 3D scan data.

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Continuous Global Navigation Satellite System (GNSS) monitoring is essential for characterizing surface subsidence in underground coal-mining areas. However, monitoring-station site selection remains strongly dependent on empirical judgment. Furthermore, steep deformation gradients can cause interferometric synthetic aperture radar (InSAR) decorrelation, phase-unwrapping failure, and data gaps in areas where ground-based monitoring is most needed. This study develops a quantitative, multi-source remote-sensing-assisted framework for GNSS monitoring-station site selection under a short-baseline real-time kinematic (RTK) configuration. Sentinel-1A, Sentinel-2C, unmanned aerial vehicle (UAV) photogrammetric products, and road-network data were integrated to construct eight evaluation factors: normalized difference vegetation index (NDVI), slope, terrain ruggedness index, deformation intensity, cumulative subsidence, InSAR coverage, distance to the subsidence edge, and distance to roads. An analytic hierarchy process (AHP) was used as the primary suitability assessment framework, while a random forest (RF) model was introduced with a limited weight to provide auxiliary information only in InSAR data-sparse areas. Grid-level aggregation, three-component Gaussian mixture model (GMM) screening, minimum-distance thinning, field reconnaissance, and monitoring-network review were subsequently combined to convert the continuous suitability surface into deployable station candidates. The resulting high-suitability areas were concentrated mainly along subsidence margins and near InSAR coverage gaps, reflecting the combined effects of deformation representativeness, supplementary monitoring demand, observation conditions, and engineering accessibility. Sixteen candidate stations were identified, of which S12, S3, and S10 were finally recommended to improve the northern, southwestern, and southeastern coverage of the existing monitoring network, respectively. Sensitivity analyses confirmed that the overall suitability pattern and recommended-station rankings remained stable under moderate parameter perturbations. Comparison with two existing GNSS stations further showed that the station with the higher suitability score exhibited a higher fitted subsidence rate (0.438 versus 0.320 mm day⁻¹). Given the two-station and two-month validation dataset, this agreement represents preliminary consistency evidence rather than statistical proof of general effectiveness. The proposed framework links regional remote-sensing assessment with site-scale engineering review and monitoring-network optimization, providing practical decision support for GNSS deployment in underground mining-subsidence areas.