The use of satellite imagery for surface deformation monitoring has been steadily increasing. However, the study of extracting deformation slopes from deformation data requires further advancement. This limitation not only poses challenges for subsequent studies but also restricts the potential for deeper exploration and utilization of deformation data. The LT-1 satellite, China’s largest L-band synthetic aperture radar satellite, offers a new perspective for monitoring. In this study, we extracted deformation slopes in Chongqing and its surrounding areas of China based on deformation data generated by LT-1. Twelve factors were selected to analyze their influence on slope deformation, including elevation, topographic position, slope, landcover, soil, lithology, relief, average rainfall intensity, and distances to rivers, roads, railways, and active faults. A total of 5863 deformation slopes were identified, covering an area of 140 km², mainly concentrated in the central part of the study area, with the highest area density reaching 0.22%. Among these factors, average rainfall intensity was found to have the greatest impact on deformation slope. These findings provide valuable information for geological disaster early warning and management in Chongqing and surrounding areas, while also demonstrating the practical value of the LT-1 satellite in deformation monitoring.

The data point target on the satellite remote sensing image of the transmission line is restricted by satellite remote sensing imaging equipment and transmission conditions, which makes it difficult to guarantee the clarity of the transmission line. Image super-resolution technology aims to recover high-resolution images from low-resolution images to improve the detailed information of the transmission line itself, which is of great significance for the intelligent inspection of transmission line satellite remote sensing and hidden danger monitoring. Aiming at the problems of traditional methods relying on multi-frame image sequences and the reconstruction results are too smooth, this paper proposes a single-frame remote sensing image super-resolution method based on the boundary balance generation confrontation network. Experimental results show that this method can provide more high-frequency information, and the reconstruction result is closest to the real image. Compared with neighbor interpolation, bicubic interpolation and other methods based on deep convolutional neural networks, the PSNR of the results in this paper is significantly improved, and effectively enhances the detailed information of the transmission line and the surrounding environment.

This article first proposes a high-precision spatio-temporal registration method between satellite remote sensing images and ground sensors. Then, using satellite remote sensing images, an intelligent identification model for typical external damage hidden dangers of transmission lines based on satellite remote sensing is established to realize intelligent identification of transmission line construction work areas and mining affected areas. Aiming at the results of intelligent identification of construction work areas and mining-affected areas, the proposed YOLOv4-based external damage identification algorithm for transmission lines is used to detect external damage hidden dangers. Through the method in this paper, it is possible to realize a regular general survey of hidden dangers of external damage (construction work area, mining affected area) with full coverage of transmission channels, and carry out targeted 24-hour monitoring on the ground. The test results show that the satellite-ground coordinated transmission line external damage monitoring and early warning in this paper. The method timely and accurately realizes the monitoring and early warning of the external breakage of the transmission line.

Collisions with overhead power lines are an important source of avian mortality, particularly for migratory and large-bodied birds. Although warning devices are widely installed to increase line visibility, their image-based visibility across viewing distances and directions remains poorly quantified. We evaluated eight power-line bird-collision warning devices using UAV-based multispectral imaging and an avian spectral-response weighting model. Images were acquired at 5, 10, 20, 30, and 50 m under upward-looking, horizontal, and downward-looking viewing geometries. A composite visibility score quantified target–background separation within the multispectral images. Raw mean visibility increased from 0.351 at 5 m to 0.484 at 50 m; however, the distance effect was not significant after accounting for viewing angle and heterogeneous residual variance. Viewing angle was the dominant source of variation at 20–50 m, with substantially higher visibility under downward-looking views than under horizontal and upward-looking views. A significant distance × viewing-angle interaction further showed that distance-response patterns differed among viewing geometries: visibility increased with distance under downward-looking views but remained consistently low under horizontal and upward-looking views. The relative performance of the tested devices also changed under low-visibility conditions, with the self-luminous tag and circular tag B attaining the highest condition-standardized mean scores. These findings identify viewing geometry as a critical determinant of warning-device visibility and demonstrate that averages pooled across observation conditions can obscure important performance differences. Evaluation and deployment should therefore prioritize device visibility from below and during near-horizontal viewing, particularly for overhead ground or shield wires at medium and long distances. This condition-specific framework provides a quantitative basis for improving the design and placement of power-line bird-collision warning devices.