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Objectives: Personalized point of interest (POI) recommendation is a vital service in locationbased social network. It can effectively use the sequence and spatiotemporal context information of check-in data to discover movement patterns and preferences of users. Methods: This paper proposes a probabilistic generative model with embedded spatiotemporal conditions to fully exploit the long-term dependency between personalized spatiotemporal preferences and sequential check-in sequences of users, constructs a gated dilation residual network, and implements a POI recommendation method based on gated dilation residual network. The method in this paper learns check-in sequences of users through a gated dilation residual network. It mines and captures the spatiotemporal patterns, sequence preferences and temporal preferences constrained by the spatial distance and time interval of sequential check-in behavior of users. Results: The proposed method shows significant improvements on the Foursquare and Instagram datasets. Compared to the best-performing algorithm NextItNet, our method demonstrates noticeable enhancements in terms of recall, precision, F1 score, and normalized discounted cumulative gain. On the Foursquare dataset, we achieve improvements ranging from 1.52% to 24.95%. On the Instagram dataset, the improvements range from 7.06% to 42.47%. Conclusions: The proposed method is more suitable for mining the long-term dependency relationships in sequential check-in behavior of users. It effectively incorporates spatial distance and temporal interval factors, thereby improving the accuracy of POI recommendation. [ABSTRACT FROM AUTHOR]

Objectives: Using satellite video data as a target tracking data source can effectively remedy the problems of fixed angle and a small monitoring range of ground surveillance video, and provide a beneficial supplement for disaster prevention, intelligent transportation, crime tracking and other aspects. However, ground objects in satellite videos are complicated. If the texture, geometry, color and other appearance features of the target are blocked and submerged by other objects, tracking drift or tracking loss will occur in the tracking algorithm. Methods: Based on the fully-convolutional siamese network (SiamFC), this paper proposes an anti-occlusion target tracking algorithm based on satellite video, which can effectively solve the tracking loss problem of moving targets under different cover-up degrees. If the target occlusion degree is low, the forward result obtained by template update network is used as the tracking result of the current frame, and the forward result is set as a new forward template to obtain the changing characteristics of the moving target. If the target is highly obscured, first, the forward result is set as the new backward template, and used to repeatedly track the target in the previous frame. Then, the spatial position prediction module is used to obtain the historical trajectory features of the target and get the trajectory result. Finally, the forward result and the trajectory result are combined to get the tracking result of the current frame based on the bidirectional verification error obtained from the repeated tracking. Results: The proposed algorithm is verified by using typical moving target, aircrafts, ships and vehicles of Jilin-1 satellite video. The results show that the proposed algorithm can provide more accurate tracking results when the target is occluded. The tracking accuracy and success rate reach 76.2% and 78.9%, respectively, and the tracking speed is 33 frame per second. Conclusions: The proposed algorithm can realize the continuous tracking and monitoring of moving targets under occlusion conditions, and can effectively improve the emergency handling ability of intelligent transportation, disaster prevention and relief, wartime monitoring and other aspects in practical application. [ABSTRACT FROM AUTHOR]

Objectives: Curve simplification is of importance in automated map generalization; nevertheless, the Douglas-Peucker (DP) algorithm popularly used in map generalization is not automatic, because a key parameter called distance tolerance (ε) must be given by experienced cartographers and needs to be input before execution of the algorithm. Methods: To solve the problem, this paper proposed a method to automatically calculate ε and by which the automation of the DP algorithm is achieved. The method consists of the following steps: (1) A formula is constructed by the Hausdorff distance for calculating the similarity degree (Ssim) between a curve at a larger scale and its simplified counterpart at a smaller scale. (2) 15 linear rivers are selected, and each of them is manually simplified to get their counterparts at seven different scales. The Ssim of each original river and each of its simplified counterpart at a smaller scale can be obtained using the formula constructed by the Hausdorff distance, and 15×7=105 coordinate pairs consisting of (S,Ssim) can be got, and a function between Ssim and S are constructed by the curve fitting using the coordinates. (3) In the meanwhile, the 15 rivers are simplified using a number of ε, and the Ssim of each original river and each of its simplified counterpart at a smaller scale can be calculated using the formula constructed by the Hausdorff distance. In this way, a number of coordinate pairs (ε,Ssim) are got, and a function between ε and Ssim is constructed by the curve fitting. (4) By the function between Ssim and S and that between ε and Ssim, a formula between ε and S can be deducted. Using the formula ε can be calculated automatically, because in a map generalization task S is usually known. After this step, automation of the DP algorithm is achieved. Results: The experiment results show that (1) The proposed DP algorithm can automatically simplify the rivers in a specific geographical area to get the results at different scales; and (2) the resulting river curves generated by the proposed DP algorithm have a high degree of similarity with the ones made by experienced cartographers. Their average similarity degree is 0.927. Conclusion: The proposed DP algorithm can simplify curve features on maps automatically, and the results are highly intelligent and credible. Although only river data is tested in this paper, the principle of the proposed method can be extended to other linear features on maps. Our future work will be on improving the accuracy of the proposed DP algorithm using more river data so that the algorithm can be used in practical map generalization engineering. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the calculation efficiency of linear octree neighbor grid elements, this paper uses Hilbert code to mark the grid elements and proposes a fast calculation algorithm for neighbor grid elements Hilbert code. Methods: Based on the Hilbert primitive curve, the concept of state vector is introduced to record the filling order of the Hilbert curve to all child elements belonging to the same parent element, thereby establishing the hierarchical evolution and degradation function of the state vector. With the hierarchical mapping relationship in the order curve, the state vector and its hierarchical evolution function are finally used to calculate the neighboring grid element Hilbert code. Results: The results of comparison experiments on calculation accuracy and efficiency show that the calculation result of the algorithm is correct. The calculation speed of the state vector decreases with the increase of the level. The calculation of 4 201 grid elements can be completed within 1 ms on the 20th level. In the calculation of the same number of neighbor grid elements at the specified level, the speed of this algorithm is significantly better than the existing Morton code conversion algorithm. At the 15th level, the speed of the algorithm in this paper is about 2.1-2.4 times that of the existing Morton code conversion algorithm. In the calculation of millions of adjacent cells at different levels, the calculation speed of the algorithm in this paper is compared with the existing Morton code conversion. The improvement factor of the algorithm increases as the level increases. At level 20, the efficiency of the proposed algorithm increases by 2.6 times. Conclusions: Experimental results show that this algorithm is superior to the existing Morton code conversion algorithm, and can be used as the basis of spatial operations such as efficient data retrieval, determination of three-dimensional topological relationship and connectivity judgment. [ABSTRACT FROM AUTHOR]

Objectives: Discrete global grid systems are the preferred data models supporting multisource geospatial information fusion. Hexagonal grids have become more popular in many applications due to their geometric characteristics within uniform adjacent. Methods: We design a uniform tiles hierarchy on the surface of the icosahedron according to the characteristics of the aperture-4 hexagonal discrete global grids, using complex numbers to build a unified coding and operation model. We also design algorithms including interoperating between geographic coordinates and codes, querying neighborhood codes. Results: The experimental results show that interoperation between geographic coordinates and codes efficiency of the proposed algorithm is approximately 2.74 and 1.73 times that of the traditional algorithm respectively,and that neighborhood codes query efficiency of the proposed algorithm is approximately 7.46 times that of the traditional algorithm. As the grid level rises, the advantages of the proposed algorithm become more obvious. Conclusions: The results of this paper are expected to provide theoretical and technical supports for the unified organization, management, processing and analysis of multi-source earth observation data. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the brain‑like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core is‑ sue is how to improve generalization ability. Methods: This paper introduces the research background and theoretical basis of the brain‑like navigation algorithm. After investigation, we propose a computational framework of brain ‑ like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods. Results： The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain‑like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations. Conclusions: In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability. [ABSTRACT FROM AUTHOR]

Objectives:In video⁃based multiple object tracking, the object detection and re⁃identification have a strong correlation. The existing methods generally train the object detection and re⁃identification net⁃ works separately, which makes the tracking speed fail to meet the real⁃time requirements. In this paper, we integrate the detection and re⁃identification into one network to accelerate the tracking process, and also solve the problems of identity switching and failure tracking. Methods:This paper develops a pedestrian mo⁃ tion model and obtains the optimal state estimation of pedestrians using center point detection. The person reidentification model with deep layer features uses the Mahalanobis distance and cosine distance to enhance the ability of person identification. And the Hungary algorithm is used for data online association, where the state estimation results become more accurate using Kalman filtering, and the unrelated lost objects are predicted by motion. Results: Experiments are conducted on MOT15, MOT16 and MOT17 data⁃ sets using the proposed algorithm and other multi⁃pedestrian tracking algorithms， and the multiple object tracking accuracy of tracking results using our proposed algorithm is 63.5, 72.4 and 70.9, respectively, and the identity F1⁃measure is optimal, with the real⁃time rate. Conclusions: The proposed algorithm can accelerate the tracking speed by network parameter sharing, and improve the recognition accuracy by per⁃ son re⁃identification training. [ABSTRACT FROM AUTHOR]

Objectives: In surveying and cartography, solving geodetic latitude is a common reverse calcula⁃ tion by conformal latitude. Methods: Several different algorithms of reverse solution are studied, including iterative method, series expansion of solving geodetic latitude by difference of conformal latitude and direct algorithm of inverse solution of geodetic latitude with conformal latitude. The calculation formulas of the lat⁃ ter two algorithms are derived by Mathematica. The higher-order coefficient expansion formula are given. At the same time, problems in existing algorithms are analyzed. Results and Conclusions: Examples re⁃ sults of Lambert conic conformal projection show that the accuracy of the formula derived in this paper can reaches (1×10−7)″-(1×10−8)″. It fully meets the requirements of high precision in surveying and map projection. [ABSTRACT FROM AUTHOR]

Objectives: Shadows in high-resolution remote sensing images will cause objects information loss and image quality decline, which is not beneficial for relative applications. Current shadow compensa- tion methods often take advantage of non-shadow information around the shadow area to increase the bright- ness, whereas there is an unsolved problem that the contrast cannot be enhanced well enough and self-adap- tively. Wallis filter principle has been used in image dodging. However, when it is used in shadow compen-sation, contrast improvement is not as good as other methods, leading to poor compensation results. There- fore, an improved Wallis model compensation method is proposed in this paper to enhance the brightness and contrast better to restore the shaded information. Methods: First, by adding compensation strength and stretch parameters, an improved Wallis model is designed. The strength parameter is positive to the bright-ness and contrast, and the stretch parameter is sensitive to the contrast. Therefore, the improved Wallis model is more efficient to adjust brightness and contrast. Moreover, an automatic parameters calculation strategy is further explored to customize a suitable compensation model for each shadow area. On one hand, the brightness average and deviation of the adjacent non-shadow region are calculated and used as the compensation target values. On the other hand, based on searching the same kinds of points around shadow boundaries, a series of non-shadow and shadow feature points are matched. Assumed the feature value of the non-shadow point is the approximate value of its responding shadow point, they can be used to calculate strength and stretch parameters automatically. Lastly, the brightness of each pixel in different shadow re-gions will be compensated by customized models to cover the shaded information self-adaptively. Results: In this paper, three sets of comparative experiments are set up to analyze and compare the part compensa- tion method's algorithm, original Wallis method in three images with ordinary object shadows and cloud shadows, respectively. The differences in brightness average and gradient average between the compensated value and the non-shadow target value are used to evaluate the compensation quality. The experimental re- sults show that: (1) Original Wallis model is useful to enhance brightness to some extent, while it is insuffi- cient to improve contrast. As a consequence, the visual compensated results are not acceptable as the other two methods. (2) Part compensation method can improve brightness and contrast effectively. However, it cannot adjust self-adaptively according to each shadow region's condition, leading to some over-compensated and some insufficient compensation results in the same image. (3) The proposed method results indicate the best compensation quality in different shadow regions because the improved Wallis model is more pointed to improve contrast and the automatically calculated parameters values are suitable to customize the com-pensation model for each shadow region. Conclusions: Aiming at the contrast improvement of the current algorithm of automatic shadow compensation, an automatic compensation method based on an improved Wallis model is proposed in this paper. The experimental results show that the newly designed model is more effective to enhance brightness and contrast, which is useful for finding suitable parameters values. Combining with the strategy of automatic parameters calculation, its customized model can adaptively com-pensate each shadow. However, it should be pointed out that there are still some limitations about the shadow border compensation and the internal difference in one shadow area, which need to be further studied. [ABSTRACT FROM AUTHOR]

With the continuous improvement of user needs and the continuous enrichment of spatial data, the same geospatial scope includes different types of spatial data such as terrain, remote sensing images, and 3D models of oblique photogrammetry. 3D WebGIS cannot effectively load different types of spatial da⁃ ta in the same scene . This paper proposes a spatial data loading optimization method for 3D WebGIS to op⁃ timize the efficient loading of multiple types of spatial data. First, using the structural features of 3D Tiles with regular enclosing volume shapes and overlapping regions, a fast growth algorithm based on binary tree is proposed to locate and extract the outer boundary of 3D model. Then, there were jagged protrusions on the outer boundary of the roughly extracted polygon. In order to solve the problem, a polygon simplification method based on two neighboring points was proposed to optimize the outer boundary of the polygon to achieve accurate extraction of the outer boundary of the 3D model. Finally, by judging the inclusion of map tile and terrain tile data relationship to achieve selective loading of tile data. Experiments show that when the viewpoint height is below 300 meters, the total load of map tile data can be reduced by about 40%, indi⁃ cating that the optimization method in this paper can significantly improve the data loading efficiency of the system without affecting the visualization of 3D WebGIS . [ABSTRACT FROM AUTHOR]

Objectives: A method of deep learning change detection with domain knowledge as an optimization strategy was proposed to improve the change detection precision of high-resolution remote sensing images. Methods: The improved change vector analysis algorithm and grey-level co-occurrence matrix algorithm were used to obtain the spectral and texture changes of images, and reasonable thresholds were set to divide the changed samples from the unchanged samples based on the spectral and texture change intensity maps. The pattern shape index and spectral knowledge in domain knowledge were introduced as an optimization strategy to filter the changed samples for obtaining high-quality training samples. The deep belief net⁃ work model was constructed and trained, and the results of deep learning change detection were optimized by the optimization strategy to reduce the influence of “salt and pepper noise” and false change zones on the detection accuracy. Results: The Results of change detection experiments show that the accuracies of Gaofen⁃2 and IKONOS images were increased by 7.58% and 14.69% and the recall by 17.08% and 23.87%, respectively, while the false alarms and were decreased by 30.22% and 23.30% and the missing alarms by 17.08% and 23.87%, respectively. Conclusions: Compared with the method before the optimization strategy was adopted, the proposed method in this paper can effectively improve the precision of change detection, and it provides a new way of using remote sensing images to improve the precision of deep learning change detection. [ABSTRACT FROM AUTHOR]

Objectives: To overcome the problem that the current bathymetric filtering methods require manual intervention and are difficult to implement technically, a bidirectional cloth simulation filtering (BCSF) algorithm is proposed and implemented in this paper. Methods: Firstly, the transfer iterative trend surface is established to eliminate the negative anomalies and guarantee the continuous expression of the seafloor topography. Then, the filtering surface is established to solve the over-filtering problem of convex and concave seafloor topographies based on the proposed BCSF correction model. Finally, to further improve the effectiveness of the filtering, adaptive distance threshold is optimized and estimated. To evaluate the performance of the proposed algorithm, the BCSF algorithm is applied to shallow water multibeam bathymetry data. Results: The experimental results show that the BCSF algorithm can avoid the over-filtering. The elimination rate of the proposed BCSF algorithm is better than that of the CSF ( cloth simulation filtering) algorithm, which decreases from 12.87% to 0.76% for the whole study area and from 15.29% to 1.09% for local study area, respectively. Conclusions: Compared with the CUBE ( combined uncertainty bathymetry estimation) algorithm, the BCSF algorithm is more easily to implement and can retain more terrain de tails. Consequently, the BCSF algorithm has strong robustness and application prospects for multibeam bathymetry data. [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 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]

Aiming at the problems during the local quasigeoid/geoid determination by traditional residual point mass model, such as large system error, big amount of basic data requirement, low computational efficiency and singularity of inverse process, a new approach is presented in this paper called semi-free position point mass based on the gravity field approximation principle. Compared with the traditional model, the plan coordinates of the new one are stationary, while the depths are free. The method is established by a kind of iterative algorithm using the relationship between the nearest points. Then, the algorithm is optimized using the weights calculated by surrounding points based on their distances from the calculating point, then to obtain the best fitting result, a kind of new adaptive method to determine the optimal weighted point number is presented. Results show that the theories presented in this paper achieve higher accuracy than traditional ways, with the advantage of little system error, less modeling data, no singularity and higher accuracy. In summary, using the method to fit local quasigeoid/geoid is feasible, and the adaptive one is better. [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]

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: 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]

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: 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]

The on ‐ board atomic clock will be affected by such factors as bad space conditions and aging equipment while it is working, so there are often outliers in the satellite clock error data, among which the additive outlier (AO) is a common class of outliers in the clock error sequence. Based on the autoregressive moving average (ARMA) model, this paper proposes an AO detection algorithm, which can not only accu‐ rately detect isolated AO, but also accurately detect pieces of AO, and overcome the overwhelming and masking phenomenon that often occurs in other algorithms. When the AO of the clock error sequence is suc‐ cessfully detected, the algorithm can obtain an accurate ARMA model, and then accurately predict satellite clock error. In order to analyze the outlier detection and prediction effect of this algorithm, BeiDou satellite clock error is used to verify it. The results show that the proposed algorithm can accurately detect the AO of the clock error sequence, and has good effects in the short‐term prediction of satellite clock errors. [ABSTRACT FROM AUTHOR]

Rotation, scaling, translation (RST) and boundary micro deformation are the main factors that affect the matching results of vector graphics, and are also good criteria for evaluating shape description algorithms. In this paper, a vector graphics matching algorithm using multi-dimensional object segmentation ratio is proposed for the influencing factors of shape matching. This algorithm extracts shape characteristic by constructing feature lines for vector graphics. It can accurately describe the shape characteristic of the polygon features to measure the shape similarity between the graphics. The vector graphics of 2 000 real geographical entities are used as the standard library for matching experiment, and half of the graphics are randomly selected as matching graphics. The matching graphics are taken to RST transformation and boundary simplification operations with varying degrees. The transformed graphics are matched with the standard library. The matching results are compared with other vector graphics matching algorithms to test the shape retrieval effect of this algorithm. This experiment demonstrate that the proposed algorithm has higher matching accuracy, RST invariance and deformation robustness. Therefore, it can accurately identify the shape of vector graphics. [ABSTRACT FROM AUTHOR]

In view of the existing machine learning methods in the field of high -resolution remote sensing image building extraction and the like, which requires positive and negative training samples to participate at the same time, a one- class building change detection algorithm based on one -class samples without the need for negative samples is proposed. Firstly, it extracts the morphological building index features of the image, and fuse multifeatures with the spectral features. Secondly, based on the one - class classification method proposed in this paper, from the object-based perspective, it gets the object-level building change detection results. Finally, it constructs a new shape feature which is refined to obtain the final building change detection result. Through experiments on multi-source high-resolution remote sensing images, it is verified that our proposed algorithm is robust and has better detection accuracy than existing building change detection algorithms. [ABSTRACT FROM AUTHOR]

Objective: The building collapse is the main cause of casualties and economic losses in all kinds of disasters. Accurate assessment of the collapse of buildings is an important basis for disaster rescue and government decision-making, and plays an important role in the proper placement of the victims, the protection of the lives and property of the victims. Therefore, the rapid assessment of building collapse after disaster is a research hotspot in the field of remote sensing and civil engineering. The main Objective: of this study was to apply the deep learning technology to automatically identify and assess building collapse and structural damage with on-site images. Methods: The building collapse assessment method used in this paper is ResNet (residual network). A ResNet-50 architecture was implemented in TensorFlow, which consists of 5 stages each with a convolution and identity block, 3 convolution layers in each convolution block and 3 convolution layers in each identity block. According to the building damage assessment standard developed by the China Earthquake Administration, the evaluation output of this model is adjusted to three types: Completely collapsed, semi-collapsed, and non-collapse. In the data augmentation process, geometry transformation and blur processing were used to simulate different shooting environments, to reduce the overfitting of the classifier model by enhancing the training image. Through data expansion, the number of training data has been increased by at least 10 times on the basis of PEER Hub ImageNet Challenge open dataset. In the process of transfer learning, the gradient descent algorithm was used to adjust and optimize the parameters of the neural network. The Results: of this method were evaluated by using accuracy and Kappa coefficient. Results: Finally, in the building collapse assessment experiment, the classification accuracy of the assessment model reached 77.23% and the Kappa coefficient was 0.65; in the structural damage assessment experiment, the classification accuracy of the assessment model reached 77% and the Kappa coefficient was 0.693.The proposed collapse classification successfully attains a relatively high rate for true-positives. Overall, this method performed well. This method shows great promise in supporting on-site decision-making for the rapid accurate assessment of building collapse after disaster. Conclusions: A novel automatic classification method for post-disaster building collapse evaluation is proposed,which meets the building damage evaluation standard. The method is demonstrated on a specific example classification focused on collapse classification and structural damage type identification.The experiment Results: show great promise in the rapid accurate assessment of building collapse after disaster. In the future we plan to improve the model to enhance the ability to extract subtle features in the image. [ABSTRACT FROM AUTHOR]

In this paper, a method of vehicular point cloud data enhancement with panoramic image is proposed. Firstly, the classification of point cloud data and the extraction of single object interested are realized by combining DBSCAN （density-based spatial clusterig of applications with noise） segmentation algorithm and typical features of objects; then, the missing area is detected and the corresponding edge is extracted one by one for a point cloud of single object; finally, a region growth dense matching based on panoramic image with local affine transform is proposed. The method is used to generate the 3D point data in the missing area, and to enhance the point cloud data. Experiment results show that this method can fill the missing area of cloud data in streetscape, and the result of point cloud enhancement is real and reliable. [ABSTRACT FROM AUTHOR]

gence of smoothing results due to the influence of ionospheric delay variation. In this paper, a moving window method is proposed to reduce the influence of unmodeled ionospheric delay, and a mathematical model is established. Formula deduction shows that moving window method can eliminate the influence of ionospheric delay trend term. In the static positioning experiment, the accuracy of this algorithm in three directions of north, east and up is 9.5 cm, 18.0 cm and 13.7 cm, which is 2.4, 1.5 and 3.6 times higher than the original pseudorange positioning results. The results show that the proposed algorithm can significantly reduce the ionospheric influence and greatly improve the positioning accuracy. It is also suitable for both static and dynamic positioning. Its optimal result accuracy is comparable to that of weighted Hatch filtering, but the window selectivity is wider and the result is smoother. On various application scenarios, the uniform smoothing window is recommended for 7 minutes.The traditional single-frequency carrier phase smoothing pseudorange algorithm is prone to diver- [ABSTRACT FROM AUTHOR]

To overcome the ill-posed problem of downward continuation for airborne gravity data, the paper proposes an alternative multi-parameter regularization method. The algorithm for selecting the regularization is proposed based on the minimization of mean squares error (MSE).In addition, the comparative analysis of the L-curve and generalized eross-validation(GCV) method of selecting the Tikhonov regularization parameter is conducted. Under of MSE, the condition that the multi-parameter regularization method is superior to the least-squares is given. By making using of the EGM2008 earth gravity model, the simulation experiment is performed. The numerical results show that the multi-parameter regularization method can guarantee the reliability and stability of downward continuation. And the multi-parameter regularization method shows the better performance than the general Tikhonov regularization method so that the feasibility of multiparameter regularization method in downward continuation for airborne gravity data is demonstrated. [ABSTRACT FROM AUTHOR]

Massive spatial data poses increasing challenges to traditional analysis software. For example, landscape pattern analysis software FRAGSTATS has been unable to process provincial‐level high‐resolution land cover data. Based on Two‐Pass connected component labeling algorithm, this paper provides an improved parallel algorithm with GPU programming to solve the landscape metrics computation problem about massive land use data. This parallel algorithm for massive landscape metrics calculation takes full advantage of a general computer, and focuses on patch perimeter and area calculation. It can also accelerate computation speed by multithreading and iteration times reduction to decrease computation time than traditional serial algorithms. We apply the proposed algorithm and serial algorithm to calculate landscape metrics of the land use classification raster images at different resolutions under patch scale.The experiment result shows great improvement of calculation performance of landscape metrics, and the efficiency has been im ‐ proved by 5 times comparing with the serial algorithm,which proves that our proposed algorithm is a better choice for landscape analysis of massive data. [ABSTRACT FROM AUTHOR]

Today, Sentinel ‐ 1A data with terrain observation with progressive scanning (TOPS) imaging mode is increasingly used in earth observation aiming at consistent monitoring of surface change and its deformation. However, due to the limited accuracy of coarse co ‐registration, spectral aliasing along the azimuth direction enables the presence of phase jumping in overlapping area of neighboring bursts. Although geometrical co‐registration in conjunction with enhanced spectral diversity (ESD) is proven to be a feasible strategy to correct such error and has been widely used in some open source softwares (e.g. DORIS (Delft object‐oriented radar interferometric software), SNAP (sentinel applications platform), ISCE (InSAR scientific computing environment)), the performance of ESD is still not satisfactory and relies strongly on spatiotemporal decorrelation. Given that decorrelation is generally quantized by interferometric coherence, this paper presents and assesses a new methodology to improve the accuracy of ESD by fully exploring the high coherent targets. Specifically, this method focuses on mitigating the spatiotemporal decorrelation in fine coregistration procedures by: (1) Selecting stable targets with moderate and high coherence using accurate coherence estimation. (2) Maximizing coherence magnitude by optimal interferometric subset chosen from Dijkstra algorithm. We compare and test this method against current favorites based on single master image and network‐based enhanced spectral diversity (NESD), and the experimental results demonstrate the value of our method. It can make up for the shortage of NESD method. [ABSTRACT FROM AUTHOR]

Line simplification is one of the most important parts of automatic map generalization. Aiming at the problem that the effect of the current line simplification methods is too dependent on the artificial setting of algorithm parameters and thresholds, and lacking the adaptive learning ability for different simplification environments, this paper considers the line simplification as a binary classification problem about the selection or deletion of the simplification unit. From the perspective of case-based studying, a new line simplification method based on support vector machine (SVM) is proposed. Firstly, point and bend are taken as the simplification unit, the simplification case is automatically obtained from the simplification results of experts, and then the feature description items are extracted as the attribute space of the case sample. The line simplification classifier is trained by SVM. Finally, the SVM classifier is used to classify the simplification units of the new line elements into selection and deletion, so that the line simplification is realized. The experimental results show that this method can well reduce the simplification intention of the experts by learning the simplification case, with a high classification accuracy for the selection and deletion of simplification units, and can adaptively complete the line simplification. [ABSTRACT FROM AUTHOR]

Coordinate transformation between quarternary triangular mesh (QTM) code and longitude/ lati⁃ tude is one of the main factors in affecting the application of QTM. However, there are significant flaws in the existing algorithms. To overcome this deficiency, an improved transformation algorithm is proposed in this paper. The main principle is as follows: According to the row and column of QTM, the address codes are recursively approached in a certain direction. Then, the transformation results are obtained precisely by introducing the operation of the relation between the judgment point and the line segment. In this algorithm, not only the transformation result is accurate but also time consumption is only about 10.1%⁃10.4% of equal ⁃triangles projection (ETP) method. In addition, the QTM code obtained in the improved algorithm still has the directionality. The improved algorithm works well for both traditional QTM and the QTM which using latitude⁃line instead of circle⁃line. [ABSTRACT FROM AUTHOR]

In received signal strength indication (RSSI)⁃based positioning, the deployment structure of bea⁃ con nodes greatly affects the accuracy of positioning. In theory, the intensive and well⁃proportioned distribu⁃ tion of these deployed can insure the high precise positioning results. However, the RSSI is seriously dis⁃ turbed by noise in the actual environment, which makes it difficult to ensure stable signal propagation even if the layout is reasonable. In addition, dense deployment can lead to crosstalk between signals, and com⁃ plex physical environments make it difficult to achieve ideal deployment of beacon nodes, which greatly af⁃ fects the positioning result. In this paper, we propose an algorithm for optimal selection of beacon nodes in trilateration positioning. After beacon deployment, the optimal reference beacon combination for each local region in the positioning scene is constructed according to the geometric relationship of the beacon nodes. Then, the beacon sequence scanned by the device in real time is matched with the regional best beacon com ⁃ bination. The preferred beacon nodes are used to solve the position, and the positioning accuracy is improved by optimally selecting the reference beacon nodes. Experiments show that the proposed algorithm can im ⁃ prove the positioning accuracy efficiently, which is close to the result of selecting the best reference beacons. [ABSTRACT FROM AUTHOR]

Aiming at the incomplete retention of features during the point cloud data procession by point cloud simplification algorithm, and data holes caused by small - curvature point cloud simplification algo- rithm, this paper proposes a new point cloud simplification algorithm integrated k - means clustering and Hausdorff distance. The topological adjacency is established in the new simplification algorithm based on the OcTree algorithm. Then the principal curvatures of all point cloud is calculated and the Hausdorff dis- tance of the principal curvatures is calculated, and the Hausdorff distance threshold set by the requirements of the reduced target is used to extracted the point cloud feature. Finally, k-means clustering is performed on non -feature regions to extract feature points, and the extracted feature points are merged to obtain re- duced results. Results show that the proposed algorithm can retain the feature information of the model more completely and avoid the void phenomena. [ABSTRACT FROM AUTHOR]

The coefficient matrix of errors-in-variables(EIV) model may contain structural features, and this situation can be extended to the observation vector. Here, we define the the augmented matrix of the coefficient matrix that consists of the coefficient matrix and the observation vector. Firstly, we reformulate the EIV model as the nonlinear Gauss-Helmert model. The augmented matrix is expanded as an affine function form, and then the random elements in it are extracted by variable projection method. Finally, we derive a novel algorithm for the structural total least squares (STLS) problem and its first-order approxima⁃ tion precision estimator based on the nonlinear least squares (NLS) theory. The proposed algorithm unifies the general STLS method， the structural data least squares (SDLS) method and the least squares (LS) method. Furthermore, this algorithm is applied to a real example and a simulation example, e.g. straight line fitting and plane fitting. The results of the two examples show that the proposed algorithm is consistent with the existing structural or weighted total least squares methods which can solve the structure problem of the EIV model, thus the results verifies the effectiveness of the proposed algorithm. The method of extracting structural features in this paper is simple in the concept and easy in the implementation. And the STLS problem is transformed into a condition adjustment problem with parameters, which is incorporated into the least squares adjustment theory system to facilitate its extended application. Additionally，the characteristic of the estimated errors of plane fitting is analyzed qualitatively. It can be seen from the analysis that the rela⁃ tive size of the parameters has a direct impact on the consistency of the estimated errors, which indicates that the consistency between the estimated errors and the true errors is relatively complex in the EIV model. [ABSTRACT FROM AUTHOR]

The generation of initial seamline network is one key step in image mosaicking. The quality of generation has a massive impact on subsequent local optimization. In this paper, a novel method for generating initial seamline network is designed. The overlapping information of effective regions of images is encapsulated in the form of a bit array and assigns to the geometric elements such as points, lines and faces. The design contributes to excavate the spatial relation between overlapping areas and to facilitate the calculation of joint priorities of overlapping areas. The main consideration of seam line generation is no longer subject to the specific shape of overlapping areas, but only the degree of overlap. Therefore, it greatly improves the robustness of seam line generation. In addition, the simple bitwise operation reduces the complexity of algorithm and thus improves the computational efficiency. This method is compared with the area voronoi diagram with overlap method. The experiments show that the proposed method has advantages in robustness and efficiency of seam line generation. [ABSTRACT FROM AUTHOR]

This paper discusses the ill-posed nonlinear least squares problem, and proposes an adaptive relaxation algorithm based on the regularization method for stabilizing the nonlinear parameter estimation. The improved algorithm achieves the adaptive selection on the regularization parameter and iterative step by using an incremental geometric regularization parameter and the minimal residual criterion. The numerical convergence experiments of the method are performed. The results show that the numerical precision of our proposed method is better than that of the linearized adjustment estimation, and the convergence property is more efficient than the iterative Tikhonov regularization method. [ABSTRACT FROM AUTHOR]

With the improvement of space geodetic observation accuracy, the geodetic inversion model is more precise and detailed, and higher requirements are also put forward for the inversion algorithm. Focusing on the inversion of fault parameters in the spherical layered model, the defects of genetic algorithm is ana- lyzed and a new combinational inversion algorithm based on iterative least squares algorithm and genetic al- gorithm is proposed. The co-seismic deformation inversion mode due to seismic fault has set up considering the earth layering. The effectiveness of the combinational algorithm has been verified using the simulated data. In the inversion of genetic algorithm, it is often difficult to obtain good results of fault depth and slip distance.. The main reason is the strong correlation between these parameters and the secondary reason is the low sensitivity of depth and slip. The combinational algorithm proposed in this paper has achieved ideal results on faults of various dip angles and has practicality. [ABSTRACT FROM AUTHOR]

This paper mainly studies the adjustment model with interval constraints，in which the adjustment problem is transformed into a quadratic programming problem with interval constraints. A new adjustment al- gorithm with interval constraints is presented, and the accuracy of the parameter solution is evaluated, in which the active set algorithm and unconstrained conjugate gradient optimization algorithm are used to parti- tion and reconstruct the quadratic problems. Because the projection gradient method can rapidly change the composition of the active constraint set, the new algorithm is more efficient than the classical active set meth- od, which can reduce the uncertainty of the model, and maintain the statistical, geometric or physical meaning of priori information, and is suitable for solving large-scale adjustment problems with interval constraints. [ABSTRACT FROM AUTHOR]

In order to generate and represent property solids with exterior topology. Based on the existing floor-plans, this paper proposes a method to adopt the way of extruding floor-plans to create 3D models of property solids, and to use 3-combinatorial maps to represent interior and exterior topology of property solids. The results are as follows; (1)A delivery method of intervals is proposed based on incidence graphs with weight and incidence matrix. (2) According to the comparison relationship between old and new intervals, a method for generating darts of 3-combinatorial maps is proposed. (3) An algorithm for adding ß relations of 3-combinatorial maps is proposed. Research conclusions show that property solids can be created by extrusion and 3-combinatorial maps, and represent interior and exterior topology of property solids and improve efficiency of constructing property solids. [ABSTRACT FROM AUTHOR]

The 3D reconstruction of building facade can be used for a wide range of applications such as urban planning, traffic navigation and virtual reality. The extraction of the geometric features (facades, windows and doors profiles) is a key step in the three-dimensional reconstruction of a building facade. In this paper, the planar structure of the building facade is first detected from point cloud data based on region growing and principal component analysis (PCA). Then, the boundaries of geometric features are defined by TIN contour extraction combined with a convex hull algorithm. Experiments show that the proposed method can effectively extract geometric features of the building facade from variable density point cloud data. [ABSTRACT FROM AUTHOR]

Based on the study of the existing real-time ionospheric estimation methods, the calculation formula for TEC using a combination of pseudo-range and carrier phase observations is proposed in this paper. Hardware delay deviation is also considered. Using simulated real-time observations from eight stations in Hubei province, estimated TEC results for the GPS and BeiDou systems were obtained for each station and compared with the GIM model. Numerical results showed that: the estimated TEC results from both systems are consistent with GIM in its magnitude and trend. In addition, the RMS of GPS was maintained within 4 TECU while the BeiDou was 5 TECU. The results show that the method can achieve real-time TEC for the BeiDou satellite navigation system and can monitor the ionospheric activity independently. [ABSTRACT FROM AUTHOR]