Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion.

Correcting atmospheric errors of Interferometric Synthetic Aperture Radar (InSAR) interferograms based on Global Navigation Satellite Systems (GNSS) data can utilize the advantages of the two measurement techniques. However, current studies have not fully considered the spatio-temporal heterogeneity of deformation and atmospheric error signals. To address this problem, an atmospheric error correction method is presented. Firstly, the error terms of the GNSS displacement time series data are corrected, and Kriging spatial interpolation is subsequently performed on the corrected GNSS displacement time series, to account for the spatio-temporal heterogeneity of the regional deformation. Then, the GNSS spatial interpolation results are weighted fused with the InSAR interferogram, to obtain a corrected InSAR interferogram that considers the spatio-temporal heterogeneity of atmospheric errors. Finally, the corrected InSAR interferograms are analyzed by Small Baseline Subset (SBAS) method, to obtain the high-precision deformation monitoring information. In order to verify the accuracy and reliability of the proposed method, GNSS and InSAR deformation data in the Southern North Island of New Zealand are processed. For the InSAR displacement time series, the results of the proposed method match well with the GNSS sites not involved in the modeling, with most of the errors below 10 mm, and the Root Mean Squared Error (RMSE) of each GNSS site are all less than 6 mm. Comparing the deformation rates between InSAR and GNSS validation sites, the differences is less than 4 mm/year at each GNSS site, and the deformation rate field contains accurate overall evolutionary trends and rich local details. Overall, the proposed method can effectively take into account the spatio-temporal heterogeneity of the deformation and atmospheric error, and it is expected to be popularized and applied in large-scale and high-precision surface deformation monitoring projects.

To accurately estimate Zenith Tropospheric Delay (ZTD) for high-precision positioning of the Global Navigation Satellite System (GNSS), this study proposes a modeling method of ZTD based on the UNB3m model and Kriging spatial interpolation, in which the optimal spatial interpolation parameters are determined based on the errors corresponding to different combinations of the interpolation parameters, and the spatial distribution of the GNSS modeling stations is determined by the interpolation errors of the randomly selected GNSS stations for several times. To verify the accuracy and reliability of the proposed model, the ZTD estimates of 132,685 epochs with 1 h or 2 h temporal resolution for 28 years from 1997 to 2025 of the global network of continuously operating GNSS tracking stations are used as inputs; the ZTD results at any position and the corresponding observation moment can be obtained with the proposed model. The experimental results show that the model error is less than 30 mm in more than 85% of the observation epochs, the ZTD estimation results are less affected by the horizontal position and height of the GNSS stations than traditional models, and the ZTD interpolation error is improved by 10–40 mm compared to the GPT3 and UNB3m models at the four GNSS checking stations. Therefore, this technology can provide ZTD estimation results for single- and dual-frequency hybrid deformation monitoring, as well as dense ZTD data for Precipitable Water Vapor (PWV) inversion. Since the proposed method has the advantages of simple implementation, high accuracy, high reliability, and ease of promotion, it is expected to be fully applied in other high-precision positioning applications.

Traditional Unmanned Aerial Vehicle (UAV) oblique photogrammetry for 3D real-scene modeling of historic cultural districts suffers from data gaps, insufficient texture, and poor accuracy in complex alleyway environments, hindering the widespread adoption of UAV technology. To address these challenges, this paper establishes a distortion region identification algorithm based on image grayscale variation range parameters. Then, through fusing UAV oblique photogrammetry, close-range smartphone photogrammetry, and Real-Time Kinematic (RTK) positioning technology, it ultimately constructs a 3D real-scene reconstruction technical framework. To validate the method’s effectiveness and reliability, a field experiment was conducted in the Zaoerxiang Historic Cultural District of Zhanggong District, Ganzhou City, Jiangxi Province, China. The experimental results demonstrate that the proposed algorithm can effectively identify distortions in the modeling results from UAV images. After fusing smartphone images from distorted regions and RTK measurements from ground control points (GCPs), the discrepancies in X, Y, and Z coordinates between the results and verification points mostly fall within 10 to 25 mm, while the differences from the measured lengths using a steel tape measure and a leveling rod were within the range of 10 to 20 mm. Furthermore, compared to approaches that rely solely on UAV images or on the fusion of UAV and all ground-based images for modeling, the method proposed in this paper restores building texture information in occluded areas and improves the accuracy of 3D real-scene modeling while simultaneously reducing data-processing and storage requirements and enhancing operational efficiency. It provides a referenceable technical framework for digital preservation, restoration planning, and smart cultural tourism of historic districts.

Tailings reservoir is an important part of the mine. The monitoring and assessment of the stability of their key structures have always been the focus of research by many scholars. In this experiment, Interferometric Synthetic Aperture Radar (InSAR) and the Global Navigation Satellite System (GNSS) were employed to obtain 30 sets of longitudinal deformation data of 6 feature monitoring points on the surface of a tailings reservoir in Anhui Province. Considering the complementarity of InSAR and GNSS in surface monitoring, it is proposed to calculate the optimal initial parameters Q and R of Kalman filter by using Particle Swarm Optimization ( PSO ), so as to construct the optimal PSO-Kalman data assimilation model to achieve bidirectional data assimilation between InSAR and GNSS data, improve the accuracy of InSAR data, and comprehensively analyze the stability of key dams. After that, the Long Short Term Memory (LSTM) recurrent neural network is utilized to conduct time series predictions on the assimilated data and the buried depth data of the saturation line of the monitoring points of the mine dam during the same period. The experimental results show that using PSO-Kalman filter for data assimilation, compared with the original InSAR data, the overall mean absolute error ( MAE ) is reduced by 52.7 %, and the overall root mean square error ( RMSE ) is reduced by 53.6 %. By using LSTM to predict the time series data, the RMSE of the key dam deformation data test output set is less than 2.5mm, and the RMSE of the saturation line buried depth test output set is less than 1.5mm. Finally, the existing data is used to train the dam stability evaluation model. Under the condition that the correct rate of the evaluation model is 88.89 % and the AUC value of the model stability is 0.88889, the LSTM prediction data and the stability evaluation model are used to analyze the future stability of the key structure. This paper innovatively proposes to jointly evaluate the stability of the key dam structure by combining the deformation data and the saturation line height data. It also presents the first relatively complete integrated research method of ' monitoring-prediction-evaluation ' for the key dam body of the tailings reservoirs. The experimental results show that this method can provide reliable data and technical support for the monitoring, stability analysis and evaluation of the key dam body of tailings reservoirs.