In recent years, remote sensing technology has been extensively used in detecting and managing natural disasters, playing a vital role in the early identification of events like landslides. The integration of deep learning models has considerably enhanced the efficiency and accuracy of landslide detection particularly in automating the analysis and quickly identifying affected areas. However, existing models often face challenges, such as incomplete feature extraction, loss of contextual information, and high computational complexity. To overcome these challenges, we propose an innovative landslide detection model, Lights-Transformer, which is designed to improve both the accuracy and efficiency. This model employs an encoder–decoder architecture that incorporates multi-scale contextual information and an efficient attention mechanism, effectively capturing both local and global features of images while minimizing information loss. By introducing a Fusion Block for enhanced multi-angle feature fusion and a Light Segmentation Head to boost inference speed, Lights-Transformer extracts detailed feature maps from high-resolution remote sensing images, enabling the accurate identification of landslide regions and significantly improving detection accuracy. Compared to existing state-of-the-art landslide detection models, Lights-Transformer offers considerable advantages in accuracy, precision, and computational efficiency. On the GDCLD dataset, Lights-Transformer achieves an mIoU of 85.11%, accuracy of 97.44%, F1 score of 91.49%, kappa value of 82.98%, precision of 91.46%, and recall of 91.52%, demonstrating its exceptional performance.

Grouting technology is widely used in construction and civil engineering, where evaluating grouting effectiveness is crucial due to the uncertainty of subsurface conditions. Existing methods face drawbacks such as destructiveness, high cost, poor durability, and limited data collection. To address these issues, this paper proposes a novel wireless real-time monitoring system based on a ZigBee sensor network framework. The sensor system integrates a direct current method in geophysics with apparent resistivity measurement to assess grouting effectiveness in real time. It consists of multichannel data acquisition units with electrodes for sensing underground currents and a user control unit for centralized management and data processing. A system acquisition performance test confirmed that the differential input channel’s equivalent input noise of the ADC was only 175 μV and 188 μV, and the average error of the captured sine wave data was 4.51 mV and 4.19 mV, ensuring the voltage measurement accuracy of the data acquisition units. Stability testing of the equipment in road and construction environments showed an average RSD of 2.86% and 2.92%, respectively, indicating good stability of the measurements. ZigBee network performance tests in three simulated environments and a field test showed that the packet loss rate (PLR) was less than 2% from 0 to 50 m, ensuring network communication in grouting project scenarios. On-site experiments demonstrate that the system can simultaneously monitor multiple profiles and perform inversions in the grouting area, which can be assembled into 3D inversion images for evaluating grout diffusion, offering valuable insights for optimizing construction operations, and enhancing grouting efficiency.

The finite-difference time-domain (FDTD) method is a robust numerical approach for the three-dimensional forward modeling of airborne ground-penetrating radar responses of complex geological structures, particularly landslides. However, standard FDTD implementations encounter significant memory demands as aircraft altitude increases and when modeling high-permittivity subsurface media (e.g., water-saturated soils), often exceeding ordinary computational resources. Existing subgridding FDTD methods, tailored for simple localized target models, are also inadequate for simulating landslide models. To overcome these limitations, we thus propose a novel high-order FDTD-based subgridding algorithm that applies coarse grids to the air layer and fine grids to the subsurface medium, enabling the simulation of arbitrarily complex landslide models with significantly reduced memory consumption. This study achieves the first implementation of the high-order FDTD(2,4) method in both coarse- and fine-grid regions, which enables larger grid sizes in both regions. As a result, the proposed approach not only preserves high-order spatial accuracy but also achieves significant memory savings. To mitigate the challenges posed by higher-order difference stencils, we introduce a specialized grid configuration with an overlap zone between coarse and fine grids, supplemented by surrounding virtual nodes. The algorithm accommodates various grid refinement factors, ensuring adaptability to dielectric models with diverse permittivity values and structural complexities. By optimizing the grid refinement factor based on the subsurface medium’s maximum permittivity, simulations can be performed with minimal memory usage. Field updates within the overlapping region are followed by weighted corrections to ensure numerical stability, whereas simulations without these novel measures exhibit oscillatory artifacts. Wavefield snapshots reveal seamless transitions across grid boundaries without spurious artifacts. Numerical experiments on deposition-type landslide models and water-bearing media confirm the validity and stability of the proposed method. Notably, using the optimal grid refinement factor reduces memory consumption to less than 8% of the standard FDTD method for aquifer model simulations.

The survey area is located in the planning area of Longquan national urban park, with frequent human activities, numerous water conservancy, transportation projects, and frequent geological disasters such as landslides. The Red Mudstone has low strength and easy to soften in the case of water, and it is very prone to landslide in rainy season. At present, there are many studies on Red Mudstone Landslides, but most of them are analyzed by geological means, and the monitoring process is also dominated by surface deformation and settlement; there is less analysis with hydrogeological that is caused by rainfall and human activities. For Red Mudstone Landslides, to effectively reveal and analyze the process of the Slip Mechanism, we need to carry out the characteristics of hydrogeological structure and underground physical structure, and compare with surface monitoring and point geological structure. In this study, a one-year field survey and hydrogeophysical survey were carried out. Combined with the characteristics of environmental geology, the geophysical forward modeling problem based on finite element method was used to verify the influence of rainfall on the electrical characteristics of underground media. The smoothness constrained least square method and three-dimensional unstructured grid method are used to interpret the ERT data, analyze the abnormal patterns of electrical structure of landslide in different seasons, and correspond well with the GPR data. According to the characteristics of surface vegetation and weathering, as well as the displacement and settlement monitoring after landslide, the spatial characteristics of red bed landslide are revealed, and the relationship between landslide, rainfall, and human activities is clarified.

The production processes for the gypsum‐ and cement‐based materials, which are widely used in civil engineering projects, require the consumption of large amounts of energy and natural resources. At the present time, the main development directions of civil engineering material include realizing solid waste recycling and the applications of green building materials, which can be then used in the fields of disaster treatment and ecological restoration, such as grouting materials. In addition, the grouting quality is always difficult to detect. This study examined the gypsum‐ and cement‐based green grouting materials made from mineral solid waste used in disaster and cloud detection by means of electromagnetic characteristics. According to the requirements of electrical testing of grouting quality, first we designed a material made with solid waste. Then, we studied the resistivity, strength, porosity, and other physical and mechanical properties of the material. The test results of the material and its soaking solution indicate that this material is a green material meeting the environmental safety standards. The experimental results show that the rapid detection of grouting quality can be realized. The following effects on electromagnetic characteristics and mechanical properties of the green materials of gypsum‐ and cement‐based were analyzed. The results showed that the resistivity and strength were related to the ratios of the water content to the modified phosphogypsum; the percentages of cement content in the total amount of modified phosphogypsum and marble powder; the percentages of marble powder in the modified phosphogypsum; the percentages of steel fiber content in the phosphogypsum; and the fineness of the marble powder. The field test results also confirm that the green grouting material can be effectively applied in engineering repair imaging monitoring.