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Objectives：To address significant geometric deformation and radiometric differences between optical and synthetic aperture radar (SAR) images, this paper proposes an automatic registration method based on spatial geometry constraint and structure feature. Methods：Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the input images. Subsequently, a local geometric correction is performed for the input image by using rational function model, which aims to achieve local coarse registration between the input image and the reference image. Then, the geometric structural feature descriptor is constructed by using orientated gradient information of images, and the feature descriptor is transformed into the frequency domain, the phase correlation is used as the similarity metric to achieve correspondences by employing a template matching strategy. Finally, the least square method is used to eliminate the mismatches based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Results and Conclusions： Three sets of high⁃resolution optical and SAR images were selected as the experimental data. The results the proposed outperform traditional methods in both matching performance and computational efficiency. The proposed method can achieve the registration of optical and SAR effectively and accurately. [ABSTRACT FROM AUTHOR]

Objectives: The gravity variation caused by hydrometeorological factors is an important factor affecting the interpretation of seismic gravity variation results. However, quantitative analysis of gravity field changes caused by hydrometeorological factors is rarely carried out. Methods: This paper takes the northern Xinjiang, China as the research area and uses the global hydrological model data of global land data assimilation system (GLDAS) and an atmospheric model to calculate the gravity influence of hydrometeorological factors on the research area from January 2016 to December 2017. Results: The results show that the annual change of terrestrial water influence is 1.3 μGal, and the difference in spatial distribution of terrestrial water influence between two adjacent periods is lower than 1 μGal. The annual change of atmospheric influence is as high as 8 μGal, and the difference in spatial distribution of atmospheric influence between two adjacent periods is up to 6 μGal. Conclusions: The gravity variations before and after deducting hydrometeorological factors were compared using the mobile gravity measurement data of three periods, i. e., April 2016, August 2016 and June 2017. The comparison results show that in the processing of mobile gravity data in the northern Xinjiang, the large⁃scale hydrological factor can be ignored and the meteorological factor should be taken into account. Meanwhile, for a better analysis of the mobile gravity variation, soil moisture and atmospheric pressure near the measuring point should be observed during the mobile gravity measurement. [ABSTRACT FROM AUTHOR]

Objectives：Continuous and dense global navigation satellite system (GNSS) surface deformation data provide an effective tool to invert refined regional surface mass variations. However, the factors influencing the reliability of GNSS inversion results need to be further studied, such as regularization (including the construction of regularization matrix and determination of optimal regularization parameter), observation noise and distribution of GNSS stations. Methods: First, we proposed an improved regularized Laplacian constraint matrix and discussed the adaptability of the generalized cross⁃validation (GCV) method in selecting the regularization parameter of ill⁃posed equations for inversion of regional surface mass variations based on the loading Green's function theory. Second, we compared the effects of different constraint matrices and constraint methods on the GNSS inversion results. Third, we further investigated the influences of different noise levels of GNSS vertical displacement, the number and distribution of GNSS stations on the inversion results. Results: (1) The regularized Laplacian matrix in this paper can better suppress the edge effects than the traditional Laplacian matrix. (2) The GCV method can effectively determine the optimal regularization parameter, and the inversion results are in good agreement with those solved by the root mean square error (RMSE) criterion. (3) If there are enough GNSS stations and the observation accuracy is high enough in the studied area, the inversion results will be more reliable. Meanwhile, the accuracy of inversion results for uniformly distributed stations is comparable to that of randomly distributed stations when the number of stations is large enough. Conclusions: The improved regularized Laplacian matrix and the GCV method can improve the reliability of GNSS inversion results, which can guide the inversion of surface mass variations using measured GNSS data. [ABSTRACT FROM AUTHOR]

Objectives: Mobile phone positioning is a widely used approach for navigation, which has broad application prospects. The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban canyons because of signal reflections or blockages. Shadow matching (SM) based on the three‑dimensional (3D) city model can effectively improve positioning accuracy in cross ‑street direction. However, variation of signal‑to‑noise ratio (SNR) is large using smartphone for GNSS signal reception while the conventional method fails to distinguish neighboring streets, hence, greater cross ‑ street errors. Methods: This paper proposes an improved SM method together with SNR smoothing implemented in smartphones to improve the positioning accuracy in urban canyons. Firstly, a SNR smoothing method based on low‑pass filter is proposed to mitigate the variation, and further to improve the correctness and stability of the visibility classification based on observations. On this basis, an improved SM, namely cluster shadow matching (Cluster‑SM), is proposed, in which, the effective candidate points are clustered related to their locations. Results: Experiment results showed that SNR smoothing re‑ duces error rate of the SNR classification from 5%-30% to 0%-20%, while the implementation of optimization Cluster ‑ SM based on SNR filtering significantly improve the GNSS positioning accuracy from 19.4 m to 2. m in dynamic test, compared to conventional method. Conclusions: This shows the effective‑ ness of the novel approach in increasing positioning accuracy with the ability to distinguish neighboring streets, which provides opportunities to implement the smartphones in location‑based services applications, pedestrian positioning or vehicle navigation which requires a higher positioning accuracy. [ABSTRACT FROM AUTHOR]

Objectives: With the development of the global navigation satellite system (GNSS), the number of visible satellites has increased, and the constellation configuration has been improved. While improving user observation information, multi ‑ system GNSS positioning increases the risk of multiple gross errors, posing a threat to the integrity of the system and restricting the application of GNSS in complex environments. A common method, the receiver autonomous integrity monitoring (RAIM) to ensure the reliability of positioning based on single error, but the reliability of positioning would decline in the case of multiple gross errors. The problem of poor detection and recognition, based on the correlation analysis method of the post‑test residual vector, the residual vector is affected by multiple gross errors, showing that the correlation with the observed feature vector of gross errors is weakened. The phenomenon makes gross error detection distortion. The aim of the study is to improving the accuracy of multiple gross errors detection by a new RAIM algorithm based on the density center of observed dataset. Methods: This paper proposes a correlation analysis method based on the density center of the observation data set to realize the detection and identification of multiple gross errors. Firstly, we construct the observation dataset through the QR calibration method. Then, we estimate the density center by the improved Mean Shift model. Finally, we test the correlation distance between the observation feature points and the density center for detection and recognition of multiple gross errors was compared. Result: The gross errors are simulated by the factual observation data, and the correlation distance of density center to gross error satellite and normal satellite, In the case of a single gross error, two gross errors, and three gross errors, the average difference correlation distance are 1.122 m and 1.516 m, 1.021 m and 1.266 m, 1.177 m and 1.588 m respectively. Compared the correlation distance of QR test vector to gross error satellite and normal satellite, the average difference correlation distance are 0.639 m and 1.142 m, 0.497 m and 0.510 m, 0.108 m and 0.198 m respectively. Conclusions: The new RAIM algorithm overcomes the problem of gross error detection distortion caused by the reduced correlation between the calibration vector and the observation vector in the presence of multiple gross errors, which can effectively improve the reliability of multi‑GNSS positioning. [ABSTRACT FROM AUTHOR]

Objectives：Accurate and stable extrinsic parameter calibrations are the basis of fusing perception and positioning of cameras and light detection and ranging (LiDAR). To address the problems of the Dhall calibration method in constraint accuracy, stability, and convergence, this paper proposed a LiDAR and camera calibration method with joint point‑line‑plane constraints. Methods：First, line‑line and plane‑plane constraints were added, and the Kabsch method was used to obtain a closed‑form solution, which reduced the influence of noise in the single corner constraint and improved the accuracy and stability of the calibration results. Then, multi‑frame point clouds were superimposed to fit lines, and the fitting corners were averaged, which further reduced the influence of the point cloud noise, thus improving the con‑ vergence speed and stability of the calibration method. Results：Experimental results show that the pro‑ posed method can converge within 20 frames, and the reprojection error of the corner points was less than 2 cm. Conclusions: The proposed method has faster convergence speed and higher accuracy than the original method. [ABSTRACT FROM AUTHOR]

Objectives: Sea ice concentration (SIC), which is defined as the proportion of a given area of ocean that is covered by ice, is a significant parameter for Arctic sea ice and climate change, an important input for regional climate model and numerical weather prediction model. The passive microwave sensors are able to penetrate the atmosphere and clouds, regardless of observation time, and their data is commonly used to retrieve SIC. Passive microwave obtained from the microwave radiation imager (MWRI) aboard on the Chinese Fengyun‑3 (FY ‑3) series satellites can be used to extract polar SIC. However, the study on its accuracy is limited. Methods: This paper focuses on the application of MWRI to Arctic SIC estimation accuracy, and compares the SIC obtained using special sensor microwave imager sounder (SSMIS), MWRI and advanced microwave scanning radiometer 2 (AMSR2) data, and utilizes Bootstrap, NASA Team (NT), fully constrained least squares (FCLS), Enhanced NASA Team (NT2) and Arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) methods with the in‑situ data. Results: Validation results show that MWRI has the smallest root mean square error (RMSE) at different spatial resolution (20.4%- 24.4%). FCLS performed better than the other five algorithms for three passive microwave data because the error was considered during the SIC retrieve, the SIC was constrained with non‑negative constrain, and numerical optimization was used to solved the SICs. In addition, MWRI performs well in summer (17.9%-23.0%) and winter (11.2%-17.8%).Conclusions: MWRI has relatively stable performance and a great potential for sea ice monitoring. Uncertainty also exists in evaluation, such as the uncertainty of accuracy of in ‑situ data in Arctic and tie points. Furthermore, scale difference between multi ‑source data could also induce the uncertainty of validation. [ABSTRACT FROM AUTHOR]

Objectives: The current railway maintenance method is unitary and simple, which is a human force intensive work, requiring tedious and interminable manual check from professional technicians. As a multi‑scale, multi‑probability and long‑term digital representation approach, the digital ‑ twin has seen a rising popularity in engineering construction. Inspired by the convenience of digital‑twin, this paper designs a simultaneous localization and mapping (SLAM) system for railway locomotives. Methods: Unlike traditional mobile mapping system which demands high precision three‑dimensional laser scanning, inertial measurement unit (IMU) / real time kinematic (RTK), as well as complicated post‑processing methods, our solution is capable of real time mapping and odometry visualization through constructed factor graph. The factor graph is a bipartite graph with two node types: Factor nodes and variables nodes, and they are always connected by edges. A new variable node is added to the graph when the pose displacements exceed a certain threshold, then the factor graph is optimized upon the insertion. We use three types of factors for graph construction: IMU preintegration factors, light detection and ranging (LiDAR) odometry factors, RTK factors. Results and Conclusions: The real time performance is further achieved through sliding window optimization. Under more than 300 km field test in various environments, our approach can achieve centimeter‑ lever positioning accuracy in feature rich and low speed cases, and the structures are clearly visible in the real time mapping result. [ABSTRACT FROM AUTHOR]

Objectives: With the rise of location service applications and the popularization of mobile smart terminals, the demand for high ‑ precision navigation and positioning is extending from isolated areas to seamless whole areas, from professional groups to mass users. However, real‑time, continuous, and complete pedestrian navigation still faces many challenges, such as occlusion of global navigation satellite system (GNSS) signals, frequent updates of Wi‑Fi/ bluetooth low energy (BLE)/geomagnetic fingerprint data‑ bases, and smoothly switching in the transitional area of indoor and outdoor. In addition, the positioning sensors of popular mobile smart terminals are limited by cost and power consumption, and the observation data is generally noisy and poor in stability. Methods: Therefore, this paper proposes a pedestrian indoor and outdoor seamless positioning technology based on cloud‑end collaboration of smartphone, which breaks through the crowd sourced collection and rapid update of the beacon fingerprint database, the precise positioning of the smart phone multi‑sensor information fusion, and the indoor and outdoor seamless switching. We have developed a smartphone positioning application for pedestrians. The collaborative precision positioning enhancement information is provided by the location service platform and the multi‑source observations data (GNSS, miniature inertial measurement unit (MIMU), Wi ‑ Fi, BLE, magnetometer, barometer) are obtained by the smartphone. Results: The feild test results show that the outdoor positioning accuracy of ordinary users is better than 1.5 m, and indoor accuracy is about 1 ‒ 3 m. Conclusions: It supports the demand of mass‑market users for seamless and precise positioning indoors and outdoors. [ABSTRACT FROM AUTHOR]

Objectives: Slow convergence has always been an important factor of limiting the development of precise point positioning (PPP). Studies have shown that the convergence speed of PPP can be significantly improved with high ‑precision ionospheric delay correction, and then to achieve PPP ‑ real time kinematic (PPP ‑RTK). At present, the ionosphere in the regional PPP ‑RTK mainly adopts the satellite ‑based ionospheric model with polynomial function (SIM_POLY) and the satellite‑based ionospheric model with in‑ verse distance weight function (SIM_IDW) for construction. Methods: In order to verify the modeling accuracy of the above two models at different latitudes, this paper first used the observation data of Guangdong, Hubei and Hebei provinces to establish ionospheric delay models, and then applied the above models to float and fixed solutions PPP under single GPS and GPS+BDS systems. Finally the results were compared with IFPPP (ionosphere‑free PPP) and CODE GIM (Centre for Orbit Determination in Europe Global Ionospheric Map, CODG) correction. Results: Experimental results showed that in the low‑latitude provinces, SIM_IDW model slightly outperformed SIM_POLY model, but there was no significant difference in the middle and high latitude provinces. Compared with IFPPP and CODG correction, PPP under SIM_IDW and SIM_POLY correction has a better performance. Besides, Hebei province achieved the fastest convergence speed compared with Guangdong and Hubei provinces, and compared with IFPPP, the positioning accuracy in single GPS solution under SIM_IDW and SIM_POLY model correction is improved by 43.7% and 43.0%, respectively. In the fixed PPP, the success rate of first epoch to fix the ambiguity of PPP‑RTK under the correction of SIM_IDW and SIM_POLY models in GPS+BDS solution could reach 86.09% and 89.13% in Hebei province. Besides, positioning accuracy could converge to 5 cm in the first epoch in horizontal direction, and converge to 10 m within 1.5 minutes in vertical direction. The positioning accuracy after convergence of GPS+BDS PPP ‑RTK had a significant improvement compared with single GPS, which was 1.3 cm in horizontal direction and 3.5 cm in three‑dimensional direction under the correction of SIM_IDW and SIM_POLY models. Conclusions: Establishing a regional ionospheric model through SIM_IDW and SIM_POLY models to realize PPP‑RTK can significantly shorten the PPP convergence time and improve positioning accuracy. [ABSTRACT FROM AUTHOR]

Objectives: The Sichuan Tibet Railway crosses the Jinsha River specifically the section from Xiongsong Town to Shadong Town, Gongjue County, Tibet Autonomous Region, in which exist strong topography variations and many potential geohazards, and hence it urgent to detect the potential geohazards in this region to ensure the railway safety. Methods: In this paper, 61 Sentinel -1 ascending, 53 Sentinel -1 descending and 7 advanced land observing satellite 2 (ALOS 2) ascending images are used to derive the annual mean deformation rates in the satellite radar line of sight (LOS) with two advanced InSAR (interferometric synthetic aperture radar) approaches, namely generic atmospheric correction online service for InSAR (GACOS) assisted InSAR Stacking and LiCSBAS. The three LOS annual mean deformation rate maps are then used to determine 2D surface movements (one along the slope and the other perpendicular to the slope) in the study region. Results: The comparison between GACOS assisted InSAR Stacking and LiCSBAS results show that they agree with each other with correlation coefficients over 0.92 for the LOS deformation rates and over 0.85 for the 2D deformation rates, suggesting the reliability of GACOS assisted InSAR Stacking. The maximum annual deformation rate of 163 mm/a can be observed in the slope direction and seven landslides (A, B, C, D, E, F, G) can be clearly identified, which in turn lays the foundation for future real-time monitoring. Based on the detailed analysis of the seven regions by using InSAR and optical interpretation results, it is found that the seven landslides had relatively obvious deformation. Landslides A, B, C and D are active landslides, which might cause the result of river blocking due to overall instability. At present, landslides E, F and G are still in the stage of slow deformation. Conclusions: This study also find that GACOS assisted InSAR Stacking can effectively remove long band and topographic related atmospheric delay error with the help of GACOS, which has the advantages of being simple, effective, fast, and easy to popularize and apply. GACOS assisted InSAR Stacking technology can be used to quickly identify potential landslide hazards. [ABSTRACT FROM AUTHOR]

Objectives: With the enrichment of heterologous image acquisition methods, heterologous image is widely used in many fields, such as change detection, target recognition and disaster assessment. However, matching is the premise of heterologous image fusion application. Simultaneously, due to the differences in imaging mechanisms of different sensors, heterologous images are more sensitive to differences in illumination, contrast, and nonlinear radiation distortion. Therefore, heterologous image matching still faces some problems. There are two main problems, heterologous image feature detection is difficult due to the difference of imaging mechanism, which indirectly increases the difficulty of matching, heterologous image has significant differences in illumination, contrast and nonlinear radiation distortion, which reduces the robustness of feature description and easily leads to matching failure directly. Methods: This paper proposes a new matching method considering anisotropic weighted moment and the histogram of the absolute phase orientation. Firstly, anisotropic filtering is used for image nonlinear diffusion. Based on this, the maximum moment and minimum moment of image phase consistency are calculated, and the anisotropic weighted moment equation is constructed to obtain the anisotropic weighted moment map. Then, the phase consistency model is extended to establish the absolute phase consistency orientation gradient. Combined with the log polar description template, a histogram of absolute phase consistency gradients (HAPCG) is established. Finally, the Euclidean distance is used as the matching measure for corresponding point recognition. Results: Several groups of heterologous remote sensing images with illumination, contrast, and nonlinear radiation distortion are used as data sources of experiments with scale invariant feature transform (SIFT), position scale orientation -SIFT(PSO -SIFT), Log -Gabor histogram descriptor(LGHD) and radiation variation insensitive feature transform(RIFT) methods, respectively. The results show that HAPCG method is superior to SIFT, PSO-SIFT and LGHD in the comprehensive matching performance of heterologous remote sensing images, and the average matching number of corresponding points is increased by over 2 times, and the root mean square error is 1.83 pixels. When compared with RIFT method, HAPCG method can achieve higher matching accuracy in the case of similar corresponding points and can realize the robust matching of heterologous remote sensing images. Conclusions: The proposed HAPCG method can achieve robust matching performance in heterologous remote sensing images and provide stable data support for multi-source image data fusion and other tasks. [ABSTRACT FROM AUTHOR]

Objectives: Leaf area density (LAD) represents the vertical distribution of canopy leaf area, and it is an important structural parameter for crop growth, crop nutritional diagnosis and breeding. Light detection and ranging (LiDAR) can detect the structural information of crop canopy by transmitting multiple pulses and receiving multiple echo signals. Methods: Firstly, unmanned aerial vehicle-LiDAR (UAV -DAR) is utilized to collect Maize point cloud data with multiple trajectories in 60 plots, which in turn is employed to estimate the leaf area density of Maize with the contact -frequency based voxel method. Then, combined with the width of the Maize leaf, the optimal voxel size is determined by analyzing multiple voxel sizes (0.2 m), and the optimal pulse incidence angle for UAV-LiDAR to get point cloud data is obtained through the comparison between different trajectories (-30° 52°). By superimposing two non orthogonal trajectorys, and comparing the two non -orthophoto graphic trajectorys, it is proved that the superposition of the two non orthogonal trajectorys can reach or exceed the effect of orthographic data. In order to improve the accuracy of the estimated LAD, Maize leaf inclination and pulse incidence angle are considered, and the estimation accuracy of leaf area density is improved compared with that before correction. With the optimal trajectory and the optimal voxel size, the leaf area density distribution of different planting densities and varieties is analyzed, the growth rate, plant type characteristics and the most reasonable planting density of different varieties of Maize plant can be obtained through the analysis of the leaf area density distribution of different plant densities and different varieties. Conclusions: It is believed that our findings in this paper can provide guidance for estimating leaf area density using UAV -LiDAR data and provide reference for Maize breeding and scientific management. [ABSTRACT FROM AUTHOR]

Objectives and Methods: Based on the mobile gravity data from 2015 to 2016 in the southern segment of the North-South Seismic Belt, this paper conducts in-depth comparative analysis of classical adjustment and quasi stable adjustment, and provides a method for evaluating the coefficient of one degree term of relative gravimeter. Two quantitative parameterized indexes, P and R, are defined with the gravity change and error, providing a quantitative basis for analyzing the significance level of the gravity changes in the network. Results: Results indicate that accuracy of gravity values is closely related to the shape of gravity network, and long branches should be connected into closed ring as much as possible before data preprocessing. The overall shape of gravity change from 2015 to 2016 calculated by quasi-stable adjustment is in consistent with that by classical adjustment, but the index (P=0.55) is lower than that of classical adjustment (P=1.70). The starting benchmark of classical adjustment is of great importance to the gravity change in the network. Under the same error of gravity change, the significance level of the gravity changes is reduced with fixed gravity value of benchmark compared to that with changing gravity value, with the index P reduced from 1.70 to 0.96.Conclusions: The quasi-stable point is crucial to the result of the quasi-stable adjustment, which determines the authenticity of the gravity changes in the network. These results can provide reference for the adjustment calculation of mobile gravity network. [ABSTRACT FROM AUTHOR]

Objectives: The cutting section analysis of three-dimensional (3D) geological model and excavation simulation of underground engineering are important functions of 3D geological modeling and visualization analysis software. The traditional explicit cutting method relies on the pre-establishment of a 3D geological body model, and the topological problem of the model is likely to lead to the phenomenon of section cavity or triangle intersection. The cutting accuracy and maximum cutting depth are limited by the modeling accuracy and depth of the existing geological body models. This paper introduces implicit functions to express multiple types of cutting planes. Methods: With a reasonable sequence setting of cutting operations, and based on the explicit calculation and attribute definition methods of the cutting section grids of the moving tetrahedron, a method that does not require any pre establishment of a complete 3D geological body model is proposed. A new implicit cutting method for quickly generating geological section from geological survey source data or geotechnical survey borehole data. Results: A cutting analysis test is carried out for 1:50 000 regional geological survey data and geotechnical survey borehole data. The results show that the proposed method can effectively avoid the instability problems of the cutting method caused by incorrect topology of geological body model. Conclusions: The proposed method supports more types of cutting planes and surfaces, controllable cutting depth and precision of cutting plane expression, which provides a new idea and reference for the spatial analysis method of 3D geological model. [ABSTRACT FROM AUTHOR]

Objectives: Ship detection in large-scale, high-resolution optical remote sensing images, especially in visible spectral remote sensing images, plays an important role in both military and civilian fields. It has always been an on-going and challenging research topic in the past decades, due to the complexity and variability of the backgrounds, the multi-scale, multi-type, and multi-posture diversity of ship targets. Methods: In order to promote the development of ship detection based on high-resolution optical remote sensing images (HRORSI), this paper provides an overview of existing study on ship detection methods of HRORSI over the past decades. Firstly, we introduce all the existing monitoring systems for ship detection, but subsequently focus only on HRORSI. Then, a number of topics have been discussed, including the methodology system, the development history, the recent state of the art detection methods, detection datasets and metrics. Results: It is shown that current ship detection methods based on deep learning in HROR-SI, have greatly expanded the adaptability of the complexity of the detection scene and the variation of the target distribution, the speed and accuracy of the results have been greatly improved. It is also found that different influence factors make a big difference in choosing the corresponding ship detection models. Conclusions: Existing methods from different perspectives such as extracting richer features, multi-level and multi-scale feature fusion, more accurate target locating, scale aware ship detection, have made vigorous performance. However, there is still a big gap between efficient and intelligent ship interpretation in actual complex applications. We suggest that the future ship detection methods should adaptively support a variety of backgrounds, scales and optical sensors, the detection model can be effectively transfer to out-distributed target domain scenarios as well as resource-constrained scenarios, and more in-depth target recognition capabilities. [ABSTRACT FROM AUTHOR]

Objectives: Network real-time kinematic (RTK) technology can provide real-time and high-precision positioning service for users over the wide range, however, the current technology depends on high density of satellite navigation and positioning (SNAP) reference station network. To satisfy the high-precision positioning requirements based on the sparse reference station network, this paper recommends a net- work RTK service method based on virtual atmosphere constrait (VAC).Methods: Firstly, a non-combined double- difference observation model is constructed to solve and fix the network RTK baseline ambiguity quickly. Then, the baseline atmospheric delay is extracted and the slant path ionosphere and zenith trop- sphere are established respectively. Finally, the interpolated atmospheric delay and its accuracy information are regarded as virtual observations to improve the positioning performance of the terminal RTK. Based on a 205.1 km station spacing SNAP network from a northwest region， China, six rover stations inside and outside the network are used for termininal RTK verification. Results: The results show that the VAC net- work RTK service method can meet the needs of high-precision positioning users over large-scale reference networks. The terminal positioning accuracy and initialization speed by the VAC method are significantly improved by 61.64% and 9.96% compared with the traditional virtual reference station method. In this service mode, the average horizontal and vertical root mean squares of the fixed solution for the six terminals are 1.19 cm and 2.73 cm. Initializing RTK solution by a lot of times, 88.78% of periods can be fixed within 2 epochs for the six rover staions. Conclusions: The VAC service mode has good adaptability to both users inside and outside the large-scale SNAP base station network. [ABSTRACT FROM AUTHOR]

Objectives: Morphology of polar sea ice changes continuously under the influences of external driving forces, e.g., wind, flow and waves. Observations and change regularity analysis on morphology of polar sea ice not only is helpful for the optimization of sea ice thermodynamics numerical simulation and the inversion of sea ice thickness based on roughness parameters, but also provide distinct importance for under⁃ standing the response of polar sea ice morphology to climate change. Methods: Based on the undulations profiles of sea ice bottom in Northwestern Weddell Sea measured by the helicopter⁃borne electromagnetic⁃ induction bird (EM⁃bird) system during the winter of 2006, this paper establishes a nonlinear statistical optimal model with the cutoff draft as the identified variable to separate the ridge keels and local undulations on sea ice bottom. Then, the keel morphology parameters and correlation between the keel draft and frequency are statistically analyzed. Finally, a new parameter T is hatched to assess the correlation between the ridge sail height and keel draft.Results: The results suggest that the optimal cutoff draft is 3.8 m and the keel spacing is the main factor affecting the ridging intensity. Despite marked sea ice deformation in different regions in Northwestern Weddell Sea, morphology changes of the keel are not evident. There is a good log correlation between draft and frequency of the keel, which vividly describes morphology and distribution of the keel. The newly hatched parameter T has a significant linear correlation with keel depth (r=0.93). Conclusions: The newly proposed modeling method can separate the keel from sea ice bottom undulations more accurately, which provides a new theoretical reference for morphological correlation analysis between surface and bottom of sea ice, and the inversion of sea ice thickness and bottom draft from its surface height. [ABSTRACT FROM AUTHOR]

Objectives: It is usually necessary to eliminate non⁃combat targets for conducting marine combat situation analysis. Because non⁃combat targets have little impacts on situational analysis and the retained combat⁃correlated targets have high reference value. Most of the existing behavior mining methods, which are based on the idea of clustering, are complicated and ineffective in cleaning of non⁃combat targets. Methods: Therefore, this paper defines multi⁃dimension record similarity (MDRS) and proposes a real⁃ time cleaning method for marine non⁃combat targets (MNCT⁃RTCM). The proposed method realizes the real⁃time cleaning of non⁃combat targets by similar duplicate record detection of multi⁃dimensional track data. Results: The experiments are carried out on simulated military scenarios, and the results are evaluated and analyzed by calculating the recall rate and the precision rate. Conclusions: The results show that the MNCT⁃RTCM method can effectively detect non⁃combat targets and achieve real⁃time cleaning of non⁃ combat targets in the marine combat environment. [ABSTRACT FROM AUTHOR]

Objectives: Traversability analysis is one of the necessary parts for rovers on extraterrestrial surface to explore unknown environment. Methods: In this paper, we propose a data⁃driven method for traversability analysis for rovers on extraterrestrial surface. Based on the inputting multi⁃dimensional terrain in⁃ formation, the proposed method models traversability analysis as a semantic segmentation problem, which can explicitly compute a traversability map of this circumstances for a specific rover. Meanwhile, we provide an algorithm for generating training dataset for the rover. We first run the rover in the field to collect di⁃ rected traversability results at certain positions，and then fulfill the undirected traversability map with these results by converting this problem into a global optimization problem, since undirected traversability map is more intuitive and straightforward for path planning. We can get the dataset for a specific rover by linking this map with the data of environment. In order to get the data more efficiently, we design an algorithm to generate virtual extraterrestrial terrains randomly and to simulate the running of a specific rover.Results:We generate a set of visible multi⁃dimensional terrain information and perform traversing test in virtual environment, which is used for generating traversibility labels in the optimization method. Based on the terrain information and labels, we train a U⁃Net⁃like network for predicting labels according to the given multi⁃dimensional information, and the network performs well on test dataset with the accuracy of 93.8% on average. Conclusions:The proposed data⁃driven method for traversability analysis is effective in virtual environment. [ABSTRACT FROM AUTHOR]

Objectives: The global navigation satellite system (GNSS) coordinate time series can be used to analyze the motion of GNSS station, and the colored noise in coordinate time series affects the estimation accuracy of the motion parameters. Therefore, in order to improve the reliability of the motion analysis, it is necessary to take effective measures to eliminate the colored noise.Methods:This paper proposes a method to eliminate colored noise by combining wavelet packet transform and information entropy theory. First, the simulation time series containing flicker noise is estimated to verify the estimation accuracy of the flicker noise. Then, discrete wavelet transform and wavelet packet transform are respectively carried out on the residual series of the simulation time series, and the flicker noise is eliminated by using information entropy. On this basis, the denoising effects of these two methods are compared. Finally, the coordinate time series at KMIN station is used as the measured data to further verify the denoising effect of wavelet packet coefficient information entropy.Results: The experimental results show that the power spectrum and the time series of colored noise eliminated by using wavelet packet coefficient information entropy are in good agreement with the original colored noise.Conclusions: The proposed method can effectively eliminate colored noise in the residual series and only retain white noise. [ABSTRACT FROM AUTHOR]

Objectives: In order to calculate the high precision gravity gradient in level 1b (L1b) data processing of gravity field and steady⁃state ocean circulation explorer（GOCE） satellite, it is necessary to accurately measure the satellite attitude angular velocity. In the coordinate system transformation of the attitude data of astrology, the low precision angular velocity components of a single astrology instrument will transmit the noise to other angular velocity components, which will affect the calculation accuracy of the whole gravity gradient.Methods: In this paper, based on the noise characteristics of the astrological instrument, the weighted matrix of the noise distribution on each axis of the astrological instrument is constructed, and the optimal attitude quaternion is solved jointly for the attitude quaternion of two or more astrological instruments, so as to provide accurate attitude control for subsequent angular velocity recovery. Results: Compared with the single astrometer calculation method, the accuracy of the optimal attitude angular velocity components wy and wz obtained by the combination of multiple astrometers can reach about 1 order of magnitude higher within 10-100 mHz, and the noise level is up to 10-6 rad/s， which can effectively suppress the noise propagation caused by the low precision angular velocity components in coordinate system transformation.Conclusions: The results show that the satellite attitude angular velocity calculated by the combination of multiple astrological instruments can be significantly improved in the whole power spectral density, the power spectral density of gravity gradient trace values calculated by the combined method of multiple astrologers is also significantly improved compared with the single astrologer method. [ABSTRACT FROM AUTHOR]

Objectives: For global navigation satellite system (GNSS), the ranging performance of observation would influence the system service capability.Methods: Parabolic antenna with large⁃diameter could greatly reduce the impact of the ground equipment and the environment on signal acquisition. Based on data from parabolic antenna, observation characteristics and ranging performance of BeiDou⁃3 global satellite navigation system (BDS⁃3) are analyzed.Results: The results show that, regarding the pseudorange deviation on BeiDou⁃3 satellite, no systematic deviation of the same magnitude, compared with BeiDou⁃2, is found on the signal of B1I, B3I, B1C, B2a, and B2b. There are fluctuations on some signals. However, the correlation with elevation angle is not strong, and the average magnitude of all satellite fluctuations is 0.1 m. This paper analyzes the multipath variation and noise level of carrier observation for BeiDou⁃3 satellite of each signal using triple⁃frequency combinations. The fluctuations of carrier phase triple combination are of the millimeter level, and those fluctuations do not closely correlate with the elevation or time. The root mean square of the triple⁃frequency carrier combination from parabolic antenna is mostly less than 0.6 mm.Conclusions: The results indicate that no systematic bias occurs in code and phase observation of BDS⁃3, and the system has a good ranging performance. [ABSTRACT FROM AUTHOR]

Objectives: The universal errors⁃in⁃variables (EIV) model extends the EIV model to the most general form, and the weighted total least squares (WTLS) algorithm is proposed to take into account the random errors in observation vector, observation vector coefficient matrix and parameter coefficient matrix. Methods: In this paper, the universal EIV function model is expanded and the second order term is included into the constant term of the model. Thus, the universal EIV model is represented as Gauss⁃Helmert model in linear form, and the linearized total least squares (LTLS) algorithm and approximate precision estimation formula of the universal EIV model are derived.Results: The estimation results of LTLS algorithm are consistent with that of WTLS algorithm of universal EIV model, which verifies the correctness and feasibility of the proposed algorithm.Conclusions: When the model contains a large number of estimators, the LTLS algorithm oftheuniversalEIV model significantly improves the computational efficiency and converges faster. [ABSTRACT FROM AUTHOR]

Objectives：In order to improve the ability of convolutional neural networks (CNNs) of understanding temporal dynamic information, this paper proposes a dominant layer optimization module. Methods: The new module uses the dominant layer to guide and optimize the update gradient of convolutional layer weights, and assist the difference estimation with the maximum mean difference algorithm of a reproducing Hilbert space. Results: In continuous training, the network can improve the learning ability of temporal dynamic information, and the dynamic information similarity between the features learned by convolutional layer and the input data is also increased. Conclusions: This module enhances the performance of the CNNs model on video human action classification and achieves improvements to the network. [ABSTRACT FROM AUTHOR]

Objectives：The load balancing of map information under multiple constraints is one of the difficulties in cartographic generalization. In small and medium ‐scale maps, it is necessary to comprehensively consider their administrative levels, topologies and metric relationships for the scale conversion of townships and village residential point features to make the map information load reasonable at a certain scale. Methods：This paper proposes a method for selecting residential point features based on optimal information entropy constraints. Under the constraints of optimal information entropy, the metric relationship constraints are adjusted, the semantic relationships are prioritized, the residential point features with higher administrative levels are reserved, and for the residential point features with lower administrative levels, if they are not the endpoints of the road and do not satisfy the metric relationship constraint, then the points are deleted, and the process is iterated until the optimal information entropy constraint is satisfied. Results： Experiments with 1∶ 250 000 residential point data have realized the selection of residential location elements that maintain topological consistency, level priority, and metric rationality. The load balancing and readability of the map are effectively maintained, meanwhile, the amount of effective information of the map is maximized based on the algorithm. Conclusions：The optimal information entropy constraint is adopted for the selection of residential points, which can retain the density characteristics of the spatial distribution of the residential point group as a whole, and achieve the load balancing of map information in effect. [ABSTRACT FROM AUTHOR]

Objectives: The scarcity of land resources has become an important factor restricting the development of some hilly cities and cutting mountains for land creation has become the most effective way for these cities to obtain land resources. However, land creation will cause serious air pollution, and how to monitor the spread of dust generated by land creation is of great significance. Methods: In this paper, remote sensing image, digital elevation model, meteorological data and air quality monitoring data are collected. Remote sensing change detection technology, wind erosion model and Aermod are combined, giving a prediction method of dust from land creation in Lanzhou northern mountain area, and experiments are carried out on the land creation in Lanzhou from 2013 to 2017. Results: From the change detection, it can be seen that the newly‑added excavation area in Lanzhou northern mountain area shows a downward trend every year, while the Lanzhou new area has always been maintained at a relatively high level. And the direction of dust diffusion caused by land creation is consistent with the dominant wind direction in Lanzhou. Conclusions: Experimental results show that the dust pollution caused by wind erosion at the land creation area will affect the air quality of Lanzhou city and the influence degree is mainly determined by the scale of land creation, wind direction and speed, and the distance between the land creation area and the urban area. When the scale of land creation decreased and the main land creation area shifts to the north, the impact of air pollution in Lanzhou decreased, while the air quality around Lanzhou new area is still seriously affected. [ABSTRACT FROM AUTHOR]

Objectives: Vector geospatial PDF (portable document format) map is a new type of electronic map, which has the characteristics of small data size, strong generality, etc., and has the function of geospatial query and calculation. Its applying needs and prospects are broad. As the method by using digital line graphic (DLG) data only, to make vector geospatial PDF map through symbolization and cartographic editing has a long process and heavy workload, it is of great significance to make full use of the existing map data to quickly produce vector geospatial PDF map. As result, it avoid affecting the existing map production process, and improve the vector geospatial PDF map making more efficient. Methods: This paper presents a quick method of making vector geospatial PDF map, which uses publication origins of map (POM) and DLG data by data preprocessing, map coordinate system construction, data aggregation and compression. (1) Data preprocessing. Based on multi‑source spatial data engine, POM and DLG data sources are loaded, integrated and utilized. Through the process of feature layer extraction, entity type extraction, coordinate extraction and attribute extraction, the useful and important objects in DLG are extracted. The POM data is processed by combining different color pages, replacing and overprinting layers, and setting the minimum page range. Select control points with known coordinates in POM page. We have completed the data preparation of vector geographic PDF map making. (2) Construction of map coordinate system. The map coordinate system model is designed and defined as a composite of six coordinate systems including DCS (data source coordinate system), GCS (geometric coordinate system), PCS (projection coordinate system), ECS (equal scale map coordinate system), FCS (file coordinate system) and OCS (output coordinate system). It also defined a series of mutual transformation parameters and functions. The coordinate system of the map is solved by knowing the coordinates of the control points and the rotation angle of the map. The coordinate registration of POM and DLG data is completed by using six coordinate system transformation parameters. (3) Data aggregation and compression. The POM with map graphical information and DLG with geographic information extracted are aggregated in the coordinate system after registration to generate a new PDF file. Using a variety of methods, the redundant data of PDF file is compressed without reducing the data accuracy. And a new vector geographic PDF map is generated. Results: This method omits the process of symbolization and cartographic editing. The experimental results show that the efficiency of making vector geospatial PDF map is high, and the map results data size is significantly compressed compared with the total amount of POM and DLG Data. Conclusions: It is suitable for large‑scale rapid production of vector geospatial PDF map with DLG and POM data source conditions. [ABSTRACT FROM AUTHOR]

Objectives: Although there have been a variety of global digital elevation model (DEM) products available for geospatial applications, the data quality of DEMs are inevitably affected by the observing techniques and processing methods. Multi ‑ source data fusion by integrating the complementary information among the datasets is an effective way to improve DEM data quality. Based on the facts, this paper proposed a point‑surface fusion model based on deep belief network (DBN). Methods: The terrain slope, land cover, and the spatial coordinates were integrated into the DBN model, thus constructed the statistical relation between the DEM elevations and the high‑quality ICESat (ice, cloud, and land elevation) GLAS (geoscience laser altimeter system) data. The DBN‑based fusion model was capable to learn the spatial structure and landscape associations of the DEM errors, and thus the multi‑source raster DEMs and the high‑quality LiDAR altimetry can be effectively fused. For the data tiles with voids, the triangulated irregular network (TIN) delta surface method was employed to merge the fusion results for the void and non‑void areas, respectively. Results: In the experiments, the effectiveness of the proposed method is tested on the fusion of ASTER (advanced spaceborne thermal emission and reflection radiometer) GDEM (global digital elevation model) v2, ALOS AW3D30 (advanced land observing satellite world 3D‑30 m) and SRTM1 (shuttle radar topography mission 1‑arc second) data, which are the most popular DEM products. Six data tiles with different terrain conditions are tested, and the accuracy of the original data and the fusion results are evaluated using the ICESat GLAS points with cross ‑ validation. Quantitative results show that the fused data have higher accuracy compared with the original datasets and the pair‑wise fusion results, both in terms of the void areas and the non‑void areas.Conclusions: The results show that the proposed method is able to im ‑ prove the accuracy of the DEM data with fusion of multi‑source altimetry datasets. Moreover, it is effective in dealing with the data voids, noise and abnormal values simultaneously. [ABSTRACT FROM AUTHOR]

Objectives: With the popularization of smartphones and the evolution of global navigation satellite systems (GNSS), navigation contributes as an indispensable function for smartphones, and the demand of high‑precision positioning for mass‑market smartphones is becoming higher. From 2016, developers can obtain GNSS raw observations through Google Android application programming interface (API). Android 9.0 and above versions, provide the option to turn off the duty cycle, which makes it possible for smartphones to conduct precise point positioning (PPP). Methods: This paper conducts quality analysis of the GNSS observations for several common‑used dual‑frequency smartphones, Huawei Mate 20/30 and Xiaomi 8, and propose a set of quality control schemes that is suitable for smartphone PPP. Gross errors can be removed by data detection according to satellite elevation, signal‑to‑noise ratio, pre‑residuals, etc., while cycle slips can be detected by the accumulated delta range state (ADRS) identifiers as well as the time difference carrier phase (TDCP) observations. A real‑time PPP application based on Android platform was developed. It supports for singe‑ and dual‑frequency, real‑time and post‑processing PPP. To verify the performance of PPP with smartphones, several field experiments were performed with different smartphones in different scenarios. Results: Field results show that: (1) The pseudorange quality of Xiaomi 8 and Huawei Mate 20 are similar, while the carrier phase quality of Huawei Mate 20/30 is significantly worse than Xiaomi 8. (2) The ADRS identifier of Xiaomi 8 can better identify the cycle slip, while it is difficult for Huawei Mate 20/30 to directly determine the cycle slips through the ADRS identifier. By combining the ADRS and TDCP can detect most of cycle slips. (3) The positioning accuracy of single ‑frequency PPP for Xiaomi 8 and Huawei Mate 20/30 reach 0.5‑0.6 m in horizontal, and 1.0‑2.0 m in vertical. The positioning accuracy of dual‑frequency PPP is better than that of single‑frequency PPP after convergence, especially in the vertical direction. (4) For the real‑time and post‑processing PPP, they both have a similar positioning accuracy in the horizontal components, while the positioning accuracy of post‑processing PPP in the vertical direction is higher than that of real‑time PPP by 20% ‑40%. Conclusions: An accuracy of a few decimeters can be achieved for real ‑ time PPP with Android smartphones. Different from geodetic GNSS receivers, the widely‑used smartphones use low‑cost linearly‑polarized antennas. In addition, smartphones are often used in urban complex scenarios, thus the GNSS data they collect have more gross errors, cycle slips and multipath effects. Further study should focus on the multipath mitigation, multi ‑ frequency and multi ‑ GNSS combination, and fusion with other sensors such as gyroscopes, magnetometers, barometers, bluetooth, Wi‑Fi, etc. to realize continuous and seamless positioning both indoor and outdoor. [ABSTRACT FROM AUTHOR]

Objectives: Inter‑satellite link of BeiDou navigation constellation has better application value in remote time synchronization among space crafts because it can eliminate a part of channel error by dual‑one way ranging method and it has a flexible link‑building way. But current inter‑satellite link system time synchronization method is highly depending on ephemeris parameters, and when one user has high orbital velocity and incomplete space position information, the conventional time synchronization algorithms are limited. Methods: This study is carried out mainly aiming at the application requirements of expanding users in navigation constellation inter ‑ satellite link technique, and an improved dynamic delay correction method based on single ‑ point pseudorange epoch conversion is proposed. The method only needs to synchronize the movement speed of one of the two sides and the position of both sides to correct the motion delay error. The advantage of this method is that the two‑way time synchronization accuracy is not affected by one user’s speed error with poor speed accuracy. In this paper, the basic theory and implementation method of the algorithm is given, and requirements to positional accuracy of spacecraft are analyzed. The algorithm is verified by simulation and test data. Results: From the simulation and test data analysis, this work could be classified some major clusters apparently: (1) By analyzing the requirement to the position accuracy of spacecraft in the course of time synchronization, the time synchronization accuracy between BeiDou ‑ 3 MEO (medium earth orbit) and spacecraft can arrive to 0.01 ns when the position error of spacecraft is less than 230 m, and the time synchronization accuracy between BeiDou ‑ 3 IGSO/GEO (inclined geosynchronous orbit/geostationary orbit) and spacecraft can arrive to 0.01 ns when the position error of spacecraft is less than 290 m. (2) By using single‑point pseudorange epoch conversion method, the time synchronization accuracy between Beidou‑3 M1 and spacecraft can arrive to 0.003 ns when the position error of spacecraft is 200 m and it has no velocity message. (3) By analyzing the inter‑satellite link measuring data between Bei‑ Dou‑3 M7 and Xi’an Ka ground station, the time synchronization accuracy between the satellite‑ground is about 0.07 ns. Conclusions: This algorithm based on single‑point pseudorange epoch conversion can solve the time synchronization with incomplete ephemeris parameters, and its time synchronization accuracy is better than 0.1 ns. This method also adapts to the precise time transferring among the low‑orbit spaces and deep‑space detectors. [ABSTRACT FROM AUTHOR]

With the development of information technology, the rise of surveying and mapping big data and artificial intelligence, the lack of data is no longer a problem. However, the existing surveying and mapping data processing technology has been pursuing the accuracy of data (micro), and big data research just allows the data to be mixed and uncertain (macro). Therefore, although the traditional surveying and mapping data processing theory has accumulated a large number of technical advantages in micro data processing, the large‑scale and complexity of big data has become increasingly prominent, in which traditional calculation model and analysis algorithm cannot effectively support the efficient analysis and processing of big data. As the key to the intelligent era, data processing theory and method, how to adapt to the challenges and opportunities of new technology is worth our deep thinking. Driven by big data, new ideas and methods such as large‑scale data mining, machine learning and deep learning are booming, which greatly promote the fusion of multi‑source heterogeneous big data inside and outside the scene, effectively extract surface feature information from a variety of sensor data, and constantly improve the ability of surveying and mapping information acquisition and analysis. We think that the theory of surveying and mapping data also needs to be followed up, and the existing data processing methods need to be intelligent. Combined with the frontier hot spots, development trends and existing challenges of intelligent surveying and mapping, this paper explores the expansion direction of data processing theory. One is to promote the further development of surveying data processing theory, and the other is to provide reference for graduate students who are interested in entering the field of surveying and mapping big data. [ABSTRACT FROM AUTHOR]

Objectives: Yan’an New District has attracted much attention because of its large‑scale mountain excavation and city construction project. Such mega‑scale land creation, rapid urban construction and complex geological conditions has induced wide land subsidence in the region. Methods: In this paper, the temporal and spatial evolution of land subsidence in Yan’ an New District was assessed using small baseline subset synthetic aperture radar interferometry (SBAS‑InSAR) to process the Sentinel‑1A images covering the period from May 2016 to October 2019. Then, the mechanism and evolution trend of land subsidence in the new district was analyzed. Results: Experimental results suggest that the spatial evolution pattern of land subsidence in Yan’an New District is highly relevant with the process of land creation project. The land subsidence area in the new district gradually expanded from the major urban area to the area of forest along with the land creation project, and its area decreased gradually with the passing of time. The temporal deformation evolution of land subsidence in the new district is related to the thickness of the fill, and all exhibit non‑linear subsidence with various velocities. The temporal evolution of land subsidence undergoes three processes of rapid‑slow‑steady. The greater the thickness of the fill, the larger the amount of sedimentation, the longer it takes for the sedimentation to stabilize. Conclusions: The main internal mechanism of land subsidence is the consolidation and compression deformation of the loess fill, which can be divided into three stages: Instantaneous settlement, consolidation settlement, and secondary consolidation settlement. Based on the characteristics of different stages combined with time series deformation, the land subsidence process and development trend can be analyzed. The research results provide a scientific reference for further monitoring and early warning, urban planning, and prevention and control of subsidence disasters in the new district. [ABSTRACT FROM AUTHOR]

Objectives: Monitoring and early warning is an important means of geohazard prevention and mitigation. In the real ‑ time monitoring and early warning of landslide, the scheduling algorithm of early warning model is directly related to the success of early warning. The traditional way is to start the early warning service regularly, which is easy to realize and is also a common practice in the industry. However, due to the influence of the time interval, there is a certain delay in the issuance of the early warning conclusion, which cannot achieve real‑time early warning. How to get early warning level quickly through early warning model and real‑time monitoring data, and how to control the time and frequency of sending early warning information are two key problems to be solved. Methods: In order to shorten the time interval from data collection to early warning information sending, and win more time for the follow ‑ up early warning processing, this paper studies the early warning process and model scheduling algorithm. Based on the timedriven and data‑driven scheduling mode of early warning model, combined with the types and characteristics of real‑time monitoring data, the scheduling strategy of early‑warning model and the release strategy of early warning information are proposed to improve the warning accuracy of landslide, and a set of early warning level solver is developed. Results: For the early warning model scheduling of displacement monitoring data, the time‑driven mode can be used in the initial stage of landslide deformation, and the data‑driven mode can be used after the accelerated deformation stage. This can greatly reduce the number of early warning calculation, and ensure that the key deformation information can be captured in time. For the state monitoring data, the data‑driven mode can be used for early warning to ensure the timeliness of early warning. For rainfall data, time ‑ driven mode has a good effect. Here, the time interval can be determined by combining the data characteristics. Conclusions: The multi thread technology is used to construct the general computing framework of the early warning model based on strategy, to realize the real‑time process tracking and early warning of landslide, and give full play to the role of landslide early warning platform. and the general computing framework of early warning model based on strategy is constructed by using multi ‑ threading technology. [ABSTRACT FROM AUTHOR]

Objectives: There are many different types of outliers in the satellite clock bias of BeiDou satellite navigation system(BDS), and these outliers directly affect the prediction quality of clock bias and the reliability of performance analysis. The preprocessing of clock bias is the precondition for the prediction and analysis of clock bias, and the recognition of outliers is the key to the preprocessing.Methods: This paper proposes a convolutional neural network(CNN)‐based recognition method for outliers of BDS satellite clock bias. Firstly, a strategy is proposed to identify outliers by graphical feature differences of clock bias. Based on the principle of image recognition, outliers with different graphic features are classified to make the judgment of outliers more consistent with human cognitive habits. Then, a CNN ‐ based recognition method for BDS clock bias is designed. Finally, by optimizing the model parameters, the accuracy of model recognition is improved continuously. Results: By using the BDS satellite clock data publicly provided by the International GNSS Service (IGS) data center, a training set and two test sets containing both phase data and frequency data are made. Under the same experimental conditions, the accuracy of phase model and frequency model in these two test sets are more than 99.6% .Conclusions: The proposed method can accurately identify the types of outliers and improve the quality of the pretreatment of clock bias. [ABSTRACT FROM AUTHOR]

Objectives: In order to study the wet temperature profile quality of COSMIC (constellation observing system for meteorology, ionosphere and climate) occultation inversion, statistical analysis of data error characteristics is needed. Methods: Firstly, the linear interpolation method is used to analyze the average deviation and standard deviation of each layer temperature of 0.2‐30 km with time window 3 h and horizontal distance 300 km as matching criteria. The temperature average deviation characteristics treat as a function of altitude, season and latitude are studied. Then，through the use of 2016 annual IGRA (integrated global radiosonde archive) sounding data, the COSMIC wet temperature profile quality in the global region is analyzed, and the characteristics of the COSMIC wet temperature profile quality in the north temperate zone are sorted out. The COSMIC occultation wet temperature profile in different latitude zones is explored. The characteristics of quality are treat as a function of latitude. Results: The results show that the average temperature deviation in the global range is -0.16 K, the deviation is lower than 0.2 K, and the COSMIC occultation wet temperature profile quality is similar to the sounding accuracy.The seasonal variation statistic F=0.999 6>0.05, this factor has no significant effect on the COSMIC wet temperature profile quality. The statistic of the latitude band change F=0.024 4<0.05, which has a significant effect on the COSMIC wet temperature profile quality. In particular, the tropical region is greatly affected by water vapor, the average temperature deviation is at the peak, the deviation is higher than 0.25 K, and the southern temperate zone is second. Conclusions: This paper carries out experimental analysis and reveals the error characteristics of GNSS occultation technology detection data, including two different dimensions of time and space, which will promote research in the fields of air weather monitoring and climate change. [ABSTRACT FROM AUTHOR]

：Objectives: In order to improve the accuracy of SAR （synthetic aperture radar） image change detection, this paper proposes a method of SAR image change detection based on variational method and Markov random field fuzzy local information C‐means clustering （MRFFLICM） method.Methods: Firstly, we fuse the logarithmic ratio images and logarithmic mean ratio images to construct the difference image. Secondly, variational denoising model is established to remove the noise from difference images. Finally, the spatial neighborhood information is introduced into fuzzy local information C ‐means clustering method by using Markov random field to improve the clustering performance.Results: Experiments on two real SAR datasets show that the proposed variational denoising method can avoid removing the small change region and effectively suppress speckle noise of SAR image. Conclusions: The MRFFLICM method can effectively improve the precision of change detection, thus enhancing the adaptability of change detection method. [ABSTRACT FROM AUTHOR]

Objectives: Road information extraction based on vehicle trajectory data is one of the hotspots and difficulties in the field of geographic information data acquisition. Traditional methods are faced with the problems of high accuracy of trajectory data source, complex road extraction algorithm model, and poor adaptability of different road extraction model parameters. In order to solve the above problems, a trajectoryroad conversion model based on conditional generative adversarial nets is proposed, and we called it trajectory‐ to‐road translation with conditional generative adversarial nets( TR‐CGAN).Methods: Firstly, the trajectory data and the corresponding road data are raster processed in the sample area to construct the trajectory‐ road sample image pairs, then the parameters of TR‐CGAN are learned based on the sample data as the prior knowledge. Through the continuous iteration of the one player game, the optimal generation result is gradually approached. Before that, according to the characteristics of vehicle trajectory data, this paper uses the control variable method and enumeration method to analyze the parameters of U ‐Net generator depth, discriminator receptive field size and objective function in the conversion model, so as to obtain the optimal structure of TR‐CGAN.Results: Using the taxi track data in the third ring road of Zhengzhou city, the experiment results show that this proposed method can find new roads more effectively. At the same time, the trained TR‐CGAN is compared with the raster road extraction method, and it is found that our method has stronger adaptability of trajectory data in both the sparse and dense areas of the trajectory, and the accuracy of generated road is higher.Conclusions: Our proposed method can realize road extraction based on trajectory data, and has better data adaptability and accuracy. In the further research, we can increase the type of sample data, so that the road extraction model can learn to generate more road types, such as ring road types. The model can be further optimized to extract two lane roads or even lane roads. [ABSTRACT FROM AUTHOR]