Ground-based synthetic aperture radar interferometry (GB-InSAR) enables the continuous monitoring of areal deformation and can thus provide near-real-time control of the overall deformation state of dam surfaces. In the continuous small-scale deformation monitoring of a reservoir dam structure by GB-InSAR, the ground-based synthetic aperture radar (GB-SAR) image acquisition may be interrupted by multiple interfering factors, such as severe changes in the meteorological conditions of the monitoring area and radar equipment failures. As a result, the observed phases before and after the interruption cannot be directly connected, and the original spatiotemporal datum for the deformation measurement is lost, making the follow-up monitoring results unreliable. In this study, a multi-threshold strategy was first adopted to select coherent point targets (CPTs) by using successive GB-SAR image sequences. Then, we developed differential GB-InSAR with image subsets based on the CPTs to solve the dam surface deformation before and after aberrant interruptions. Finally, a deformation monitoring experiment was performed on an actual large reservoir dam. The effectiveness and accuracy of the abovementioned method were verified by comparing the results with measurements by a reversed pendulum monitoring system.

Crack detection based on deep learning is an advanced technology, and many scholars have proposed many methods for the segmentation of pavement cracks. However, due to the difference of image specifications and crack characteristics, some existing methods are not effective in detecting cracks of containment. To quickly detect cracks and accurately extract crack quantitative information, this paper proposes a crack detection model, called MA_CrackNet, based on deep learning and a crack quantitative analysis algorithm. MA_CrackNet is an end-to-end model based on multiscale fusions that achieve pixel-level segmentation of cracks. Experimental results show that the proposed MA_CrackNet has excellent performance in the crack detection task of nuclear containment, achieving a precision, recall, F1, and mean intersection-over-union (mIoU) of 86.07%, 89.96%, 87.97%, and 89.19%, respectively, outperforming other advanced semantic segmentation models. The quantification algorithm automatically measures the four characteristic indicators of the crack, namely, the length of the crack, the area, the maximum width, and the mean width and obtains reliable results.

It is challenging to measure, analyze, and control the deformation and space attitude of antennas in narrow radomes through geometric measurement to ensure the accuracy of phased array radar application. In this manuscript, a method for rapid measurement of antennas by adopting the combination of photogrammetry system, laser tracker and total station was proposed. The photogrammetric method was used with a homemade auxiliary tool to measure and calculate the antenna’s deformation under different temperatures and pitch angles. Mapping relations between the instrumental coordinate systems of photogrammetry system, laser tracker and total station were established through common points, including target balls and reflectors. Coordinates of mark points on antenna surface were converted from photogrammetric coordinate system to laser tracker’s and total station’s coordinate system. Thus, antenna’s pitch angles relative to the local horizontal plane and deflection angles relative to the north of the engineering coordinate system were calculated. Planar fitting, coordinate transformation and space attitude calculation were all carried out in the Spatial Analyzer (SA) software. The photogrammetric method detected the antenna’s gravitational deformation sensitively, the maximum Root Mean Square Residual (RMSR) of the reference length was $54~\mu \text{m}$ , and that of the mark points was $59~\mu \text{m}$ , which was stably in line with its nominal accuracy. Deviations caused by coordinate transformation had no significant effect on the calculation of antenna’s spatial attitude, for the maximum deviation between the converted and measured coordinates in laser tracker’s coordinate system was 0.139 mm, and that in total station’s coordinate system was 1.037 mm, both of which were within reasonable limits of the derived theoretical maximum deviations between different instruments. As a result, the maximum deviation between the calculated value and servo system’s nominal value of antenna’s pitch angle was $59.4''$ , and that of antenna’s deflection angle was $91.87''$ . This method’s efficiency was greatly improved by about ten times compared with traditional methods through statistics and estimates.

In this paper, we present an image acquisition device for building surface. To position the image and generate an unfolded view of the building, the camera needs to be orientated to determine the camera’s pose before image acquisition. In this paper, we propose an automatic orientation method based on target point recognition. Traditional orientation methods often have low success rates and poor accuracy. Our method divides the orientation task into three steps: detecting the prism and its centre point sequentially, and finally completing the orientation. To address the challenge of detecting prism center points (PCPs), we add the scale sequence feature fusion and ScaleConcat modules based on YOLOv8s, achieving multi-scale feature fusion to enrich semantic and positional information. Additionally, adding a small object detection head enhances the detection accuracy for small targets such as PCPs. Experimental results demonstrate that our method achieves a mean average precision (mAP) of 94.68% for prism detection, while achieving precision and recall of 89.12% and 88.40% respectively for PCP detection, with a mAP of 92.68%. Furthermore, our method exhibits high orientation accuracy, with horizontal and vertical angle detection errors of just 4.61 ′ ′ and 2.93 ′ ′ respectively. The proposed model offers fast orientation speed, high success rates, and precise orientation, meeting the requirements of practical applications.

Thermal anomalies serve as potential earthquake precursors and are crucial for understanding the mechanisms underlying seismogenic mechanisms and geodynamic perturbations. To address the limited understanding of the polarity evolution of thermal anomalies, we developed a dynamic spatiotemporal adaptive framework to quantify global thermal anomaly responses. Four parameters—the coefficient of determination (R²), spatiotemporal uncertainty (SU), temporal–spatial uncertainty ratio (TSUR), and spatiotemporal correlation coefficient (SCC)—were established to characterize the spatiotemporal patterns of thermal anomaly responses. Additionally, the Anomaly Emphasis Proximity (AEP) was introduced to identify statistically significant thermal anomaly events. The results indicate that the spatiotemporal evolution of thermal anomalies exhibits a transition from pre-earthquake mixed anomalies (both positive and negative) to post-earthquake unipolar anomalies (TIB decreased from 92% to 49%), accompanied by pronounced sea–land differentiation (SST increased from 0.3% to 98.7%). The AEP reveals significant thermal anomaly clustering highly consistent with earthquake activity (e.g., the 2008 Mw 8.0 Wenchuan earthquake in the Qinghai–Tibet Plateau), showing strong correlations in structurally active regions (e.g., SCA and SWS; FDR < 18.5%, STCW > 3.7%) but weaker ones in stable regions (e.g., CNA and ECA). Overall, this framework significantly enhances the robustness and reliability of seismic thermal anomaly detection.