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Aiming at the problem of image registration difficulties caused by the significant differences of multi-source remote sensing images due to the sensor type, temporal phase and illumination conditions, this paper proposes a multisource image registration method guided by first-order Gaussian steerable filters. Methods: First, based on the geometric reference information of the image, the overlapping area of the reference image and the image to be aligned in the image space is calculated, the overlapping area is uniformly partitioned with the reference image surface as the reference, the corresponding image block to be aligned is calculated by the rational function model and the digital elevation model, and the affine transformation model is established to geometrically correct the image block to be aligned, so as to realize the coarse registration between the local images. Second, for feature detection, anti-cluster features from accelerated segmentation test improved by the chunking uniformity checking strategy are constructed to obtain a large number of uniformly distributed feature points. And for feature description, a set of multi-scale, multi-directional first-order Gaussian steerable filters are constructed to convolve the image, and by pooling the convolution results to achieve feature dimensionality reduction, a multi-source consistent feature description is constructed. Finally, feature matching is performed based on the nearest-neighbor principle, and high-precision correspondences are obtained by eliminating mismatches. And bundle adjustment is further performed based on the rational function model to calibrate the rational polynomial coefficients of the images to be aligned and geometrically correct the images, so as to realize the fine registration of the images. Results and Conclusions: Experimental results using multiple pairs of satellite multisource images show that the accuracy of the proposed method is better than 1 pixel on multitemporal optical data, optical-infrared data, and 1.5 pixels on optical-synthetic aperture radar data, and the computational efficiency is more than doubled compared to the existing similar methods. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter to measure the material and energy exchange between soil and atmosphere. It is a key environmental factor in the fields of hydrology, meteorology, agriculture and so on. Remote sensing inversion and model simulation are two basic means to obtain global soil moisture data. Remote sensing inversion can obtain soil moisture data with relatively high resolution, but there are often time-space gaps while land surface model can simulate spatiotemporal continuous data, but the spatial resolution is often coarse. Both two data cannot meet the fine observation of soil moisture. Therefore, it is very important to develop an effective method to obtain high-resolution, spatiotemporal seamless soil moisture data. Methods: Focusing on their complementary advantages, we propose a global seamless soil moisture data generation method integrating microwave remote sensing and model simulation. Specifically, for the vacant area of 9 km soil moisture data of soil moisture active passive satellite, GLDAS Noah 0.25° model assimilation data is introduced to establish the spatiotemporal fusion model between them. Through this model, Noah data is downscaled to 9 km to fill the vacant area. As for some areas that have not been filled, Noah data is used for fitting, interpolation and filling, and the residual correction is further carried out based on Poisson equation method, and then the spatiotemporal seamless 9 km daily soil moisture data is generated. Results: We evaluated the experimental results by simulation experiment evaluation, site data evaluation and triple collocation evaluation. Experiments show that the global seamless soil moisture data produced in this paper performs well in all evaluation methods. Conclusions: This method can effectively combine the spatial resolution advantage of remote sensing observation with the spatiotemporal continuity advantage of model simulation, provide spatiotemporal seamless global soil moisture data, and better meet the needs of global scale water cycle monitoring and water resources management. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the climate change in Xinjiang region, China is complex. It is of great significance to study the terrestrial water storage (TWS) and its load deformation for the accurate maintenance of the regional reference frame. Methods: The satellite gravity data GRACE (gravity recovery and climate experiment) was used to invert the changes of land water storage in Xinjiang region from 2010 to 2014, and the independent component analysis was used to decompose it into spatiotemporal models, and the spatial characteristics of multiple time scales were analyzed. On this basis, the correction on the first-order of gravity potential coefficient and load Love number is carried out, the correction of the non-tidal atmospheric and ocean model (GAC) from GRACE is introduced to obtain the vertical change of the crust caused by the migration of the land water load. The GRACE inversion results are corrected by scale factor method derived from WaterGAP global hydrology model (WGHM). The influence of deformation is analyzed by introducing monthly precipitation data from global precipitation climatology project. And the deformation displacements of the 13 continuously operating reference system stations in the survey area are quantitatively compared. The correlation between TWS load signal of each station and the annual vertical displacement of global navigation satellite system (GNSS) is analyzed. Results: The results show that TWS in Xinjiang region presents multi-time scale characteristics, with obvious annual variation and long-term trend. The annual change is particularly significant near the western Pamirs Plateau, and the trend term is reflected in the long-term decrease year by year. The signal is relatively strong in the western Urumqi and Tianshan Mountains. In general, the change of land water load causes the vertical displacement of the crust in the study area, and the time series shows a small fluctuation with an amplitude of -1.5-1.5 mm, which is consistent with the deformation results of WGHM model. In addition to the obvious time lag, the time series of TWS load deformation and precipitation change are consistent with the overall trend. After GAC correction, the consistency between GRACE results and GNSS vertical displacement was enhanced, and the correlation coefficient and weighted root mean square (WRMS) were significantly improved. The correlation coefficient of each station was 0.63-0.91, and WRMS was 16.18%-58.97%. Conclusions: The research method in this paper can reveal more detailed hydrological signal characteristics in Xinjiang region, and the research results can provide important technical support for accurate maintenance of regional reference frame. [ABSTRACT FROM AUTHOR]

Objectives: As an important grain-producing area and the most populous plain area in China, the groundwater resources in the North China Plain are seriously exploited for agricultural irrigation and domestic water use, and it is of great ecological significance and socio-economic value to accurately monitor the changes of groundwater storage in the region and continuously recharge and restore the groundwater. Methods: The gravity satellite data were used to invert the groundwater storage changes in the North China Plain, and compared and analyzed with the hydrological model, precipitation information, South-North water transfer project and groundwater well information, and proposed a method to integrate shallow groundwater storage changes and deep groundwater changes to obtain the contribution of total groundwater. Re⁃ sults: The results show that: (1) Both gravity satellite and global land data assimilation system model estimated water storage changes in the North China Plain exhibit obvious seasonal characteristics. (2) The correlation coefficients between the gravity satellite inversions and the shallow groundwater well estimations are consistent in terms of interannual variability, multi-year trend variability and seasonal variability, with correlation coefficient greater than 0.8. (3) Considering the contribution of deep groundwater, the estimated results of integrating shallow and deep groundwater well information and the gravity satellite inversions are in better agreement than those using only shallow groundwater, and indicate that shallow groundwater is the main variation of groundwater storage change. (4) From 2003 to 2017, groundwater in the North China Plain is in a long-term deficit state, with a gravity satellite estimated groundwater deficit rate of-1.3± 0.6 cm/a. From 2018 to 2020, the gravity satellite estimated groundwater deficit rate is −1.9±0.7 cm/a, which is almost consistent with the total groundwater deficit rate of -1.8±0.8 cm/a during the same period taking into account the deep groundwater; the groundwater trend in 2021 is increasing, and the growth rate of gravity satellite inversion is 12.7±1.8 cm/a, which is more consistent with the total ground-water well estimation in the same period. The correlation coefficient is 0.98, and the weighted fused total groundwater storage change is more consistent than that estimated by using only shallow groundwater. (5) Precipitation and North-South water transfer project have a direct impact on water storage changes in the North China Plain, making a significant contribution to the mitigation of groundwater storage deficits and groundwater restoration. Conclusions: The research method in this paper provides a new way to accurately observe groundwater storage changes in the North China Plain, and provides reliable verification of gravity satellite monitoring results. [ABSTRACT FROM AUTHOR]

Objectives: Individual driving destination prediction has important value in location-based services such as personalized services recommendation and intelligent transportation. However, most of the existing deep learning methods construct travel features on the basis of densely-sampled trajectory points, which generate data redundancy but only achieve limited information gain, and in turn introduce negative impacts on model training. Road intersection sequence can simplify the expression of driving trajectories restricted on the roads, and reduce the model training cost. Meanwhile, transfer preference between intersections and current movement mode imply the spatiotemporal correlation between road intersections and destinations, and are capable to represent individual travel intention to some extent, but few researches have applied them to destination prediction. Methods: This paper proposes a novel individual driving destination prediction method that takes intersection transfer preference (ITP) and current movement mode (CMM) into account, named ITPCMM. First, it constructs input features in units of each intersection, and adopts graph attention network to learn the individual-level transfer coefficients between intersections in different time slots. Then, it uses long short-term memory (LSTM) to capture the long-term dependence of transfer preferences. Second, ITP-CMM constructs time cyclic encoding features and driving features to learn current move mode by using two-layered LSTM. Third, intersection transfer preferences and current move mode are fused and weighted through feature intersection and attention mechanisms respectively. And the stacked residual networks are introduced to output prediction results. Results and Conclusions: Based on the trajectory data of twelve private car drivers in Shenzhen City, China for the whole year of 2019, this paper has conducted a group of experiments to verify the feasibility of the proposed method ITP-CMM. We verify the effectiveness of the proposed method by carrying out ablation experiments and comparing the prediction accuracy and stability of ITP-CMM with four baselines, including hidden Markov model, LSTM, distance neighboring dependencies model and attentionaware LSTM for real-time driving destination prediction by considering location semantics and location importance trajectory points. Besides, we demonstrate the value of transfer preference in capturing the spatial correlation between intersections through visual analytics, and explore the impact of the number of transfer intersections on the prediction accuracy. [ABSTRACT FROM AUTHOR]

Objectives: Disaster event evolutionary graph can comprehensively express the development process of disaster, information of sub-events and multiple event relationships, providing knowledge services for disaster analysis. The problems in the construction of disaster event evolutionary graph include the lack of Chinese corpus containing spatiotemporal relationship, imprecise event extraction due to fuzzy boundaries of Chinese vocabulary, and difficulty in recognizing implied event relationships. Methods: This paper proposes a method for constructing disaster event evolutionary graph based on spatiotemporal relationship. First, a vocabulary enhancement event extraction model based on naming regularity (VENR) is designed to discern entity name patterns, and determine event information boundaries and types. Second, a relation recognition model by fusing attention and gateddilated convolution (AGDC) is proposed to obtain multi-dimensional text features, thereby uncovering potential event relationships. Additionally, a Chinese disaster corpus with spatiotemporal relationship (CDCSTR) is established. Results: We experiment on CDCSTR and Chinese emergency corpus (CEC), the results show that the F1 scores of VENR are 88.59% and 78.49%, respectively on CDCSTR and CEC. Compared with the existing methods, AGDC improves the performance of four tasks on CDCSTR, especially excelling in spatial relationship recognition, achieving a performance lead of over 3.08%. Conclusions: The Chengdu-Leshan expressway tailgating accident event evolutionary graph is built for verification, enabling the effective expression of event evolution and spatiotemporal features in real disaster scene, consequently supporting emergency responses to disaster. [ABSTRACT FROM AUTHOR]

Forecasting trends in sustainable sustainability levels in metropolitan areas serves as the foundation for developing development policies that are tailored to local conditions. Sustainability evaluation studies based on a multidimensional comprehensive index system fall short of highlighting the variability, systemic, and dynamic nature of metropolitan area development. To better understand the role of multidimensional scenarios in guiding the sustainable development of metropolitan areas, a greater emphasis on suburban spatial units and spatial heterogeneity characteristics is required. Methods: We simulate the trends of sustainable development levels of 16 municipal districts in Beijing, China from 2011 to 2030 by combining the system dynamics model and the shared socioeconomic pathways, and thus condense the metropolitan area's sustainable development model and compare the advantages and shortcomings of each development scenario under different models. Results: The findings show that: (1) Beijing's municipal districts perform differently in economic, social, and environmental subsystems, and there are three types of typical districts: Synergistic development, ecologically biased development, and economic constraint. (2) Shared socioeconomic pathway (SSP) 1 is the most desirable urban sustainable development scenario among the four scenarios of SSP1, SSP2 (BAU (business-as-usual)), SSP3, and SSP5. (3) In order to achieve the transition from the historical trajectory to the SSP1 scenario, each district type should pursue a distinct development strategy. To improve the quality of coordinated development, synergistic development type zones should use social security and employment expenditures to compensate for the shortcomings of social development. Ecologically biased zones should reduce energy consumption and increase environmental protection investment to maintain environmental advantages, and economic constraint type zones can maintain high gross domestic product growth rates in the short term to get out of the development dilemma. Conclusions: The findings of this paper highlight the complex cause-and-effect relationships between urban economic, social, and environmental systems and offer a framework for system evaluation and simulation technology for the sustainable development of metropolitan areas. This framework will serve as a source of regulations and a foundation for management of urban refinement. [ABSTRACT FROM AUTHOR]

Objectives: Recognition of building group patterns is an important part of the building generalization. Efficient methods for the recognition of building group patterns can effectively improve the quality of automated map generalization. Traditional recognition methods mainly include geometric methods and traditional machine learning methods, which are limited by the complex rule definitions and huge feature engineering. The graph convolution neural network (GCN) can overcome the limitations of traditional methods to some extent and has been successfully applied to the pattern analysis of buildings. However, there are few methods to recognize multiple building group patterns using GCN, and the existing GCN methods are almost based on spectral graph convolution, which do not consider local spatial information fully. Methods: As the spatial GCN is more efficient than the spectral GCN, this paper introduces a spatial GCN method DGCNN to recognize three building group patterns, including linear pattern, grid pattern and irregular pattern. The first step is to cluster the buildings into different groups, then some indices are selected to construct the feature vector of each group according to the Gestalt principles. Second, Delaunay triangulation (DT) and minimum spanning tree (MST) are chosen to construct graph structures of building groups. Finally, graph structures are used as input for the DGCNN model, and building group patterns are obtained after training. Results: The experiment selects the urban areas in Shanghai and we compare the accuracies with traditional spectral GCN method. The results show that the accuracy for the DGCNN model in the train set can reach 97.06%, and 95% for the test set. And the accuracies for the spectral GCN model in the train set is 90.90%, and 86.05% in the test set. Conclusions: The proposed method overcomes the limitations of spectral GCN, and it does not need graph Fourier transform. It can improve the recognition accuracy significantly and faces less difficulty in explaining the process of feature extraction. Hence it is an effective method for the recognition of building group patterns. [ABSTRACT FROM AUTHOR]

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Objectives: High definition map (HDM) is the “sensor” for automated driving (AD), which integrates the real-time data collected by various sensors and the prior data collected in the early stage, and serves the application of AD, including providing the necessary prior data and the corresponding over-the-horizon perception, calibration positioning, dynamic planning and decision-making and control. However, there still exists a gap between the supply of HDM data and the demand for AD knowledge, including difficulties in data retrieval due to the large volume of data and challenges in semantic understanding and intelligent decision-making due to weak data correlation. Therefore, how to balance the data supply of HDM and the knowledge demand of AD is the main goal of this paper. Methods: Knowledge graph (KG) is a representation of knowledge in a graph structure to describe entities and their relationships, involving entity extraction and relationship extraction, so that it can make the AD with interpretable, understandable and inferential. This means that KG can serve as an alternative and explicit simulation of the human mind and map cognition that driverless vehicles are missing. Therefore, we introduce KG on the basis of HDM data, and propose a framework of HDM-KG-driving task, so as to support the transformation of HDM data to AD knowledge. The construction of HDM-KG adopts the top-down method, that is, the pattern layer is first followed by the data layer. As the conceptual hierarchy of KG, pattern layer defines the concept, attribute and relation of map ontology, and it can explicitly describe the indirect information and implicit correlation information of map domain from the perspective of traffic. The data layer is an instantiation of the pattern layer, a way to populate the pattern layer through instance matching. Results: To verify the validity of the proposed method, a virtual simulation dataset based on OpenStreetMap data is constructed and converted into two formats. The first is the OpenDRIVE format, which is used to build the KG of static data, including the static knowledge of roads, lanes, intersections, road signs and road markings. The other is the data format of CARLA AD-simulator, which is used for AD simulation and the construction of dynamic real-time KG, including the dynamic knowledge of self-driving cars and vehicles in front of them, pedestrians and other traffic participants. Through the application of the resulting HDM-KG in semantic query, knowledge reasoning and local decision planning, the results show that the HDM-KG explicitly expresses the semantic relationship, enhances the application of HDM in AD, and can further improve AD by connecting additional knowledge such as safety requirements, traffic rules or scene background. Conclusions: HDM-KG can structurally and explicitly express the traffic semantics contained in map objects and the semantic relations between objects, traffic rule constraints, traffic scenes and other knowledge, and realize the three-layer evolution of data-information-knowledge of data sources-HDM-KG, providing support for semantic relation query, logical reasoning and motion planning required by driving tasks. In addition, the introduction of KG eliminates the need for driving tasks to perceive and predict objects and semantic relationships from data collected by sensors, which are explicitly defined by HDM-KG, reducing computational latency and improving safety and reliability. The top-down method of constructing KG will cost more labor and time, and the subsequent research will carry out the bottom-up method to automatically fill the KG with multi-source data. [ABSTRACT FROM AUTHOR]

Objective: Deep convolutional neural network (DCNN) is widely used in automatic road extraction from high-resolution remote sensing images (HRSIs). However, the existing methods make it difficult to model the context relationship of pixels in the predicted results. To solve this problem, some studies have used fully connected conditional random field (FullCRF) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. Methods: In order to improve the integrity of road structure, this paper proposes a short range conditional random filed (SRCRF) model combined with DCNN. SRCRF mainly includes a unary potential function based on road pre-segmentation, a binary potential function based on spectral spatial features, and a K-neighborhood mean field inference algorithm. First, the prior knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF. Then, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency,that is, adjacent pixels with similar spectral features have the same label. Finally, the K-neighborhood mean field inference algorithm based on the mean-field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors. Results: The experimental results show that SRCRF not only alleviates the transition smoothness of FullCRF, but also alleviates the structural discontinuity and incompleteness in the road acquisition results of HRSIs. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1-scores of SRCRF are increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCRF. Conclusions: The proposed SRCRF combines the advantages of DCNN, optimizes the fully connected structure of traditional conditional random fields into the K-neighborhood structure, reduces the inference scope, and improves the proportion of feature vectors. Compared with FullCRF, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. The performance of SRCRF is improved compared with DCNN and FullCRF, and the time is shortened by one order of magnitude compared with FullCRF. In the future, we will further investigate the potential for learning Gaussian features and investigate more complex architecture of conditional random field to better capture global context information. Additionally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc. [ABSTRACT FROM AUTHOR]

Objectives: The traditional methods of identifying urban features use spatial and statistical algorithms to extract analysis indicators, but feature evaluation indicators are very subjective. Street view images contain visual information of the city and can be used to identify urban features. Methods: Taking Qingdao, China as an example, this paper proposes a multi-scale semantic segmentation model, named MS-DeepLabV3+, based on street view images. The proposed model adds full feature extraction channels in the encoding process to aggregate multi-scale features, and adds multi-scale feature extraction channels in the decoding process to effectively capture low-level features. And convolutional block attention module and efficient channel attention modules focusing on key features are introduced to improve the accuracy of semantic segmentation of street views. The mean intersection over union, accuracy and recall of the proposed model have been increased by 3.47%, 2.37% and 3.96%, respectively. We build a multi-dimensional feature vector of the city in six dimensions, including environment dimension, facility convenience dimension, economic affluence dimension, transportation dimension, urban safety dimension and urban synthesis dimension. Based on the semantic segmentation results of the street view images, the data are combined with the point-of-interest data and residential land use data. At the plot scale, we extract the feature vectors and calculate the values in six dimensions to characterize the urban features of each urban area in Qingdao. This paper uses the Grad-CAM method for interpretable analysis of semantic segmentation models and the feature attribution SHAP method to mine the intrinsic drivers of multi-dimensional features in cities. Results: Different urban areas have different feature vectors, and the feature vectors of different urban areas have the advantages in specific dimensions. Conclusions: The above analysis helps optimize the multi-dimensional features in urban space for the planning and construction of cities. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the inversion accuracy of cylindrical oil tank height from satellite imagery, this paper introduces a height inversion model of oil tank using satellite imagery with the same name arc distance (SNAD). Methods: First, we define the concept of SNAD and clarify several main geometric elements and the physical meanings of oil tank on satellite image. Then, considering the isotropic characteristics of the cylindrical oil tank, we propose a height inversion model based on imaging simulation under multiple strategic conditions. Finally, we clarify each parameter in the inversion model and deduce the calculation method of key parameters. In order to verify the accuracy and applicability of the proposed model, we invert the height of two types of oil tanks in four high-resolution satellite images and compare with the conventional methods. Results: The proposed model can effectively realize the detection of SNAD and height inversion of cylindrical oil tank in multi-satellite images. The root mean square error of the proposed model ranges from 0.50 m to 1.00 m, with an average value of 0.78 m, while that of the conventional model ranges from 9.28 m to 9.59 m, with an average value of 9.44 m. The standard deviation of the proposed model ranges from 0.35 m to 0.63 m, with an average value of 0.46 m, while that of the conventional model ranges from 6.50 m to 6.69 m, with an average value of 6.60 m. Conclusions: The proposed model has the advantages of high inversion accuracy, excellent stability, strong universality, convenience and efficiency. It can provide a new solution to carry out oil tank height inversion based on high-resolution satellite image. [ABSTRACT FROM AUTHOR]

Objects: Mountain peak extraction technology determines the accuracy of the hill position classification and the efficiency of automatic classification of micro-landform. Because of the limitations of elevation and contour lines, such as loss of local mountain peaks and incomplete removal of false mountain peaks, other landform factors could be used to express the distribution feature around the mountain peaks. Methods: Based on the uniform distribution feature of the aspect around the mountain peaks, this paper proposes an efficient mountain peak extraction model, in which the aspect is centered on the mountain peak and gradually increases clockwise. Moreover, according to the ridge line fitting method and the recursive thought of depth first search algorithm, the false mountain peaks in the extraction results are removed, while the real mountain peaks are retained. Considering the negative influences of the terrain fragmentation of the entity digital elevation model(DEM), this paper experiments and analyzes with simulated DEM and entity DEM respectively. Results:The results show that the method overcomes the uncertainty of subjective threshold based on closed contour lines. The mountain peak extraction accuracy of simulated DEM can achieve 100% due to its continuity and smoothness. Considering the terrain fragmentation of entity DEM will make it ill-posed, we improve it by adjusting the aspect distribution constraint condition and obtain average extraction accuracy of 96.1%. Conclusions: An efficient mountain peak extraction model is proposed based on the digital terrain analysis technology and the uniform distribution feature of the aspect around the mountain peaks, and the topographic feature points are extracted from the perspective of terrain geometry. Compared with the traditional mountain peak extraction methods, the proposed method is relatively accurate and simple, and can effectively reduce the false mountain. [ABSTRACT FROM AUTHOR]

An introduction about the application, evaluation and funding of various projects of the National Natural Science Foundation on Geodesy in 2023, is made in this paper. A statistical analysis is conducted from the perspective of the research direction and supporting institution of the applied projects, and a word cloud analysis is carried out for the keywords of the applied projects. A total of 534 project applications in geodesy were received in this year, with a growth of 3.1% over 2022, 116 of which were funded by the end of October 2023. The result of the keyword cloud analysis shows that the current hotspots in geodesy mainly focus on global navigation satellite system, interferometric synthetic aperture radar (InSAR), BeiDou satellite navigation system (BDS) and their related research. Satellite precision positioning, orbit determination, navigation and application, InSAR technology and its application, as well as study on material migration and climate change in the Tibet Plateau, constitute the three major research sections in geodesy. [ABSTRACT FROM AUTHOR]

Objectives: Taylor series expansion is often used in the downward continuation of potential field, and its performance depends on the accuracy and reliability of vertical partial derivatives or radial partial derivatives (RPDs) of potential field parameters. Methods: In order to avoid the singularity on spherical boundary and the uncertainty to the computational results by using the closed analytic kernel function to solve the partial derivative, first, this paper considers the fact that all kinds of gravity observations behave as a type of limited spectrum bandwidth signal after being filtered, and proposes to express the kernel function of the Poisson integral for the external gravity anomaly by a spherical harmonic series expansion, which is truncated into a band-limited summation with the same spectrum range as the gravity observation. Then, we derive a set of band-limited formulas to calculate the high-order RPDs, which are modified and applied to the downward continuation of the gravity anomaly by Taylor series expansion. Results and Con⁃ clusions: The formulas are validated using the ultra-high-degree geopotential model EGM2008 by a twostage procedure. The numerical tests of the band-limited formulas and the Taylor series expansion downward continuation model show that the proposed band-limited formulas have good reliability and validity, and are superior to other models in terms of stability and accuracy. [ABSTRACT FROM AUTHOR]

Objectives: With the rapid popularization of low-cost receivers, single-frequency global navigation satellite system (GNSS) is now widely used. The cycle slip rate of a single-frequency GNSS receiver is commonly high in a complex situation. Although geometry-free (GF) model can detect some large cycle slips, it is not sensitive to small cycle slips. The geometry-based (GB) model is efficient for cycle slip detection and repair, but its success rate of detection descends correspondingly with an excessive number of abnormal satellites. Methods: By combining the advantages of GF and GB models, this paper proposes a new cycle slip detection and repair method. The proposed method firstly detects and rejects the abnormal satellites with large cycle slips by GF model, then uses GB model to detect small cycle slips and repair large cycle slips. Results: The results show that the proposed cycle slip detection and repair method can enhance the success rate if many cycle slips occur simultaneously. The success rate can be improved by approximately 50% even when the abnormal satellites reach the half or more of total numbers simultaneously. Conclusions: Compared with the traditional GB model, the proposed GF+GB model can take advantage of each model to improve the performance when multi-cycles occur. [ABSTRACT FROM AUTHOR]

Objectives: To analyze spatial disparities in urban living service facilities based on supply and demand service levels, we not only detect the spatial distribution characteristics of facilities, but also gain indepth insights into the underlying causes of these differences. Methods: This paper specifically focuses on Jianghan district in Wuhan city and conducts a case study by using the food market as a representative facility. The analysis involves constructing spatial disparity in accessibility of services (SDAS) index to assess the disparities between the supply and demand of life service facilities. Subsequently, SDAS is optimized by an improved gravity model based on the road traffic network and income levels of residents. Results: The results show that most community units in Jianghan district exhibit a high level of disparity between supply and demand, with notable differences observed in Wangjiadun central business district. The service capacity of life service facilities around communities significantly influences SDAS value under various conditions. The multi-mode case provides a more comprehensive reflection of the spatial differences between the facility accessibility and the needs of community units, offering a nuanced view of the actual situation. Conclusions: The results contribute to shaping city structure and facilitating optimal allocation of life service facility resources. It can provide scientific basis for layout optimization of public facilities in urban area. [ABSTRACT FROM AUTHOR]

Low spatial resolution is one of the main bottlenecks restricting the fine application of remote sensing parameters. Spatial downscaling is an effective way to improve their spatial resolution and application capabilities. Researchers have developed various downscaling methods for different remote sensing parameters. However, a unified and general method classification system has not yet been formed. Based on the in-depth commonality analysis of the downscaling of various parameters, this paper systematically summarizes the spatial downscaling methods from the perspective of data fusion, taking into account the required complementary information. Four types of downscaling methods are summarized: Multi-parameter fusion, spatiotemporal fusion, remote sensing-model fusion, and super-resolution reconstruction implicit fusion. The advantages, disadvantages, and applicable scenarios of various methods are analyzed, and the development trend of spatial downscaling method research is discussed. It can provide theoretical and technical support for improving the refined application ability of remote sensing data. [ABSTRACT FROM AUTHOR]

Objectives: When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud. Methods: This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data. Results: For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.219 3 to 0.270 5. Conclusions: The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest. [ABSTRACT FROM AUTHOR]

Objectives: The quality of network structure is the key to obtain excellent classification performance. Most of existing deep models are designed by the trial-and-error method. When the same model processes different hyperspectral images (HSIs), different network hyperparameters need to be set artificially to adapt to the data characteristics of different images, greatly increasing the difficulty of network design. Aiming at the problem of deep learning model design in hyperspectral image classification, this paper proposes an automatic network structure search method for HSI classification. Methods: In the proposed method, the differentiable structure search technology is first used to search the network structure on the source HSI data sets, then the deep network model is constructed by stacking cells, and finally the classification performance is evaluated on the target HSIs. It should be pointed out that, the proposed method only conducts one search on the source HSI data sets, and the obtained deep model can be applied to other target HSI classification tasks, which can effectively improve the utilization rate of the model. To improve the generalization ability and classification accuracy of the model, multi-source and multi-resolution HSIs are employed to construct the precollected data set, and the partial channel connection operation is introduced to further improve the search efficiency. Results: Three HSIs inculding Houston, Chikusei and Pavia Center are selected as source data. Four HSIs including University of Pavia, Indian Pines, Salinas and Houston 2018 are selected as the target HSIs. The source data is used to determine the network model and the target HSIs are used to evaluate the classification performance. Experimental results show that the proposed method can automatically search out a universal deep network model suitable for HSI classification task, and this model can achieve better classification performance than the conventional deep learning models. The overall classification accuracies of University of Pavia, Indian Pines, Salinas and Houston 2018 are 98.15%, 98.74%, 97.30% and 74.47%, respectively. Conclusions: Compared with other deep models, the model in this paper is automatically determined by differentiable architecture search algorithm according to the data characteristics of HSIs, effectively improving the classification accuracy of target HSIs while avoiding complex network design. Extensive experiments verify the applicability and effectiveness of the proposed method in HSI classification. [ABSTRACT FROM AUTHOR]

Objectives: Lanzhou New Area is an important window for the development of northwest China. In recent years, the large‐scale construction of mountain excavation and city construction project has led to different degrees of land subsidence, which has seriously threatened the economic and social development of the new area. Methods: Taking Lanzhou New Area as the research object, this paper uses small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) to process 58 ascending Sentinel-1A image data covering the study area from May 2017 to February 2021. The distribution characteristics and spatio temporal evolution of land subsidence in the study area were obtained, and the SBAS-InSAR measurement results were verified by field investigation. Then the related factors affecting land subsidence were discussed. Results: The results show that land subsidence in Lanzhou New Area is mainly concentrated in mountain excavation and city construction area, with an area of about 33.5 km² and a maximum subsidence rate of-68 mm/a. In space, with the development of excavation and filling projects, the land subsidence develops to the southeast of the new district, and the land surface presents a long‐term continuous settlement process. Conclusions: Land subsidence is closely related to many factors such as mountain excavation and city construction project, thickness of excavation and filling, geology and road. Specifically, mountain excavation and city construction project control the spatial extent of land subsidence, and then the filling thickness control the magnitude of land subsidence. The nature of artificial fill is an intrinsic factor causing land subsidence, while road construction will also accelerate the process of land subsidence. The research results can provide scientific basis for the sustainable and safe development of the new district and urban planning and construction. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, a great breakthrough has been made in the text generation image problem based on generative adversarial networks (GAN). It can generate corresponding images based on the semantic information of the text, and has great application value. However, the current generated image results usually lack specific texture details, and often have problems such as collapsed modes and lack of diversity. Methods: This paper proposes a determinant point process for generative adversarial networks(GAN-DPP) to improve the quality of the generated samples, and uses two baseline models, Stack-GAN++ and ControlGAN, to implement GAN-DPP. During the training, it uses determinantal point process kernel to model the diversity of real data and synthetic data and encourages the generator to generate diversity data similar to the real data through penalty loss. It improves the clarity and diversity of generated samples, and reduces problems such as mode collapse. No extra calculations were added during training. Results: This paper compares the generated results through indicators. For the inception score, a high value indicates that the image clarity and diversity have improved. On the Oxford-102 dataset, the score of GAN-DPP-S is increased by 3.1% compared with StackGAN++, and the score of GAN-DPPC is 3.4% higher than that of ControlGAN. For the CUB dataset, the score of GAN-DPP-S increased by 8.2%, and the score of GAN-DPP-C increased by 1.9%. For the Fréchet Inception Distance score, the lower the value, the better the quality of image generation. On the Oxford-102 dataset, the score of GANDPP-S is reduced by 11.1%, and the score of GAN-DPP-C is reduced by 11.2%. For the CUB dataset, the score of GAN-DPP-S is reduced by 6.4%, and the score of GAN-DPP-C is reduced by 3.1%. Conclusions: The qualitative and quantitative comparative experiments prove that the proposed GAN-DPP method improves the performance of the generative confrontation network model. The image texture details generated by the model are more abundant, and the diversity is significantly improved. [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: Using battlefield text data for spatiotemporal topic analysis, we can obtain the spatial distribution pattern of geographical environment elements and their impact characteristics on battlefield activities from micro to macro and from scattered to gathering places, and mine the spatial distribution and development law of battlefield events, which further enriches the perception mode of battlefield environment and provides a new means of battlefield environment efficiency analysis. It is of great significance and value for in-depth understanding of battlefield environmental knowledge. Methods: The key technology to improve the quality of spatiotemporal topic discovery in geographical environment is to effectively construct entity composite relationship network and integrate multi-dimensional heterogeneous relationships for topic clustering. First, a spatiotemporal topic tensor model integrating multi-dimensional relationships is constructed, and the complete expression of topic relationship is given by using the Tucker decomposition of topic tensor model. Then, the feature vector space of multi-dimensional relational clustering is constructed as the objective function of topic classification, and the block value matrix decomposition technology is used for joint clustering calculation, and the core tensor matrix is used to solve the problem of data sparsity. Finally, the block value matrix obtained by multi-dimensional relational clustering is used to obtain the association value between geographical environment elements and spatiotemporal topics. Results: The results show that: (1) The geographical environment entities and entity relationships are correctly clustered into spatiotemporal topic structure. The accuracy rates in the training set were 88.4% and 86.9% respectively, and in the test set were 87.3% and 85.8% respectively. (2) The number of entities and tags clustered under different subject structures decreases gently with the reduction of subject scale. The statistical results show that the most subject tags are maneuver, attack and interception, and the most location tags are highlands, roads and villages. (3) Compared with latent Dirichlet allocation (LDA) algorithm, the multi-dimensional relationship joint clustering method can be seen that the number of entities and labels mined by this algorithm is generally higher than LDA algorithm, so it can be seen that the accurate and clear space-time topic structure can be obtained after integrating multi-dimensional relationships. (4) The block value matrix obtained by multi-dimensional relationship clustering reflects the internal characteristic relationship of the spatiotemporal topic structure of the geographical environment, indicating that the spatial-temporal theme has strong cohesion. Conclusions: This method can effectively improve the quality and efficiency of spatiotemporal topic discovery, making the obtained topics better show the cohesive correlation between geographical environment elements, geographical location and event topics, providing a basis for clearly reflecting the evolution process of spatiotemporal topics, and supporting for mastering the development trend of events. Since this paper only takes the co-occurrence frequency of entity words as the weight value in the construction of relationship matrix, there is a certain deviation in data analysis. In the future, we will combine the attention mechanism to dig deep into multi-source text data, improve the efficiency and accuracy of data analysis, and establish the temporal and spatial correlation between different hot spots on the basis of effectively discovering cohesive hot spots, for inferring the event change process and providing an important reference value for the dynamic deduction of battlefield environment. [ABSTRACT FROM AUTHOR]

Objectives: Co-seismic deformation monitoring is of great significance for the interpretation of co-seismic deformation characteristics and intuitive understanding of fault geometric characteristics. For large earthquakes with surface rupture, global navigation satellite system(GNSS) technique has a low spatial resolution, and interferometric synthetic aperture radar(InSAR) technology will cause phase decorrelation due to large deformation gradients, it is impossible to obtain specific deformation around the fault. Optical image correlation(OIC) and pixel offset tracking(POT) based on sub-pixel cross-correlation can solve these problems well. Methods: The main idea of the sub-pixel cross-correlation method is to use the pre-event image in the two images as a reference, and then compare the post-event image with it to evaluate the similarity between the two images, and then retrieve the displacement between the homonymous points. OIC can obtain east-west and north-south deformations, and POT can obtain range and azimuth deformations. In this paper, the deformation in each direction of the Kaikoura earthquake obtained from Sentinel-1 data and Sentinel-2 data is used to form different combinations, and the least square method is used to calculate the three-dimensional deformation. Results: The combination of OIC+POT_As_Des has the best constraint. The accuracy of the north-south deformation obtained by OIC+POT_Range is not as good as that of OIC+POT_As_Des. Compared with POT_As_Des, the north-south deformation precision obtained by it is higher, and the deformation performance on the right side of the Kekerengu fault and the left side of the Papatea fault is also better. In the northeast of the Kaikoura earthquake epicenter, very complex and huge surface deformation and multiple ruptures were detected in two close areas, and the vertical defoamation was mainly uplift. Conclusions: For Sentinel-1 and Sentinel-2, the combination of OIC+ POT_As_Des is most suitable for obtaining the three-dimensional deformation of the Kaikoura earthquake. This earthquake is a dextral strike-slip earthquake with reverse. [ABSTRACT FROM AUTHOR]

Objects: It is tried to understand the adjustment from an information theoretic viewpoint. Methods: Besides the concepts of covariance matrix, cofactor matrix and weight matrix, which are often introduced in “Surveying Adjustment” courses, we introduce the concept of Fisher information matrix (or simply information matrix) in this paper. Results: Several important properties of the information matrix are shown; the population and the sample information matrices are distinguished. The information matrix under the assumption of multivariate normal distribution is derived; the links among the population/sample information matrix, the population/sample covariance matrix and the cofactor/weight matrix are explained; and it is revealed that the weight matrix can be viewed as a normalized information matrix. Based on the information matrix, the concept of information vector is introduced; the adjustment in information domain is derived in which the information matrix and the information vector are calculated rather than the parameter vector and the covariance matrix calculated in standard adjustment. Though equivalent with the standard adjustment in terms of the results, the information-domain adjustment has simpler format and clearer structure; this provides a new perspective for understanding the traditional adjustment; more importantly the information-domain method has special merits in solving under-determined model and also in initializing the recursive adjustment method. Finally, the dynamic adjustment in information domain is derived for filtering a dynamic state space model; it is nothing but the information filter which is equivalent to the standard Kalman filter. Conclusions: Information theory provides new insights for comprehensively and thoroughly understanding the surveying adjustment theory. [ABSTRACT FROM AUTHOR]

Global navigation satellite system-reflectometry (GNSS-R) is a kind of promising remote sensing technique, which utilizes the reflected signals of GNSS for geophysical parameters detection. Its applications on ocean surface are earlier and more mature. However, in recent years, with the development of spaceborne satellite exploration programs such as TechDemoSat-1, cyclone GNSS (CYGNSS) and Fengyun 3E,its advantages and potential in land remote sensing research have gradually emerged. The research work of the existing mechanism model is summarized. Meanwhile, this paper reviews the research status of spaceborne GNSS-R and other signal of opportunity reflection remote sensing (SoOP-R). GNSS-R's applications on soil moisture, vegetation, soil freeze-thaw monitoring are focused on, while the research status of the latest SoOP-R technology in root zone soil moisture and snow water equivalent is also summarized. In this way, we hope to promote the development of this technology in the detection of major climatic and meteorological parameters for the hydrological cycle to a certain extent. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-interferometric reflectometry can be taken advantage of identifying soil moisture of land surface. Methods: Aiming at the problem of multi-GNSS combined soil moisture inversion, we use the phase extracted from the signal-to-noise ratio observation data of global positioning system (GPS), BeiDou satellite navigation system (BDS), GLONASS and Galileo system, and solve soil moisture inversion within consideration of satellite track clustering by using the empirical model. The estimation of multi-GNSS combination is obtained by weighted average method. Results: The inversion accuracy of BDS and Galileo is equivalent and superior to GPS and GLONASS. The root mean square error of the method in this paper is 0.041 4 cm3 /cm3, which is about 16.3% and 5.2% lower than that of single navigation satellite system and the optimal frequency band respectively. Conclusions: The results indicate that the multi-GNSS reflection signal estimation method based on track clustering can effectively monitor changes in soil moisture. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-reflectometry (GNSS-R ) is a new method for measuring soil moisture with rich signal sources, which utilizes GNSS reflected signals for detecting the physical parameters of the reflector with cheap cost and high spatial resolution. Methods: Considering the fact that in the traditional GNSS-R soil moisture retrieval, the data of land cover types is ignored or simpli fied, this paper makes detailed labels about the land cover types based on the definition of international geo sphere biosphere programme (IGBP) considering the similarities and differences between different land cover types, and uses back propagation (BP) neural network to establish soil moisture inversion models. The main work is to replace the IGBP number labels with refined feature labels. After the extraction, matching and quality control of cyclone global navigation satellite system (CYGNSS) and soil moisture active passive (SMAP) data, the dataset is randomly divided into training set and testing set. Then BP algorithm is used for training neural networks to establish soil moisture retrieval model. Results: The correlation coefficient between retrieved soil moisture and reference value is 0.85, and the root mean square error (RMSE) is 0.060 cm3 /cm3 . Meanwhile, the soil moisture obtained by CYGNSS and SMAP have good spatial and tem poral consistency, but the inversion performance varies on the different land cover types. On the barren or sparsely vegetated land type, the RMSE is smallest and on the evergreen ncedlcleaf forest land type, the RMSE is largest. Meanwhile on the shrublands, grasslands and croplands land types, the inversion effect is better than permanent wetlands, urban/built-up and cropland/natural vegetation mosaic land types. Com‐ pared with the existing IGBP numbering and labeling method, the accuracy has been improved by 7.7%. The precision improvement of deciduous broadleaf forest, closed shrublands and woody savannas types is significant. Conclusions: The proposed method of refined land type digitization can effectively retrieve soil moisture without relying on other auxiliary data such as vegetation opacity and surface roughness, and it is feasible and effective. [ABSTRACT FROM AUTHOR]

Objectives: Spaceborne global navigation satellite system-reflectometry （GNSS-R） has been demonstrated to be effective in retrieving global sea surface wind speed, and many missions have been launched in orbit or planned. “Jilin-1” Kuanfu-01B satellite, as a commercial remote sensing satellite of China, equipped a GNSS-R instrument to observe Earth's physical parameters. This paper aims to preliminarily analyze and demonstrate the effectiveness and feasibility of GNSS-R data from “Jilin-1” Kuanfu01B satellite for retrieving sea surface wind speed. Methods: Three basic observables sensitive to wind speed, namely the delay-Doppler map (DDM) peak signal-to-noise ratio (SNR), the leading-edge slope and trailing-edge slope of the normalized delay waveform, are extracted from the DDMs of “Jilin-1” Kuanfu-01B satellite, and are used to demonstrate and assess the potential in retrieving wind speed. Three basic observables have obvious inversely proportional relationships with wind speed; therefore, an empirical exponential function is used to develop the geographic model function (GMF) between the basic observables and wind speed. Furthermore, the root mean square error (RMSE) between the retrieved wind speeds and European Centre for Medium-Range Weather Forecasts (ECMWF) data are obtained. Results: The RMSE of the retrieved wind speed using DDM peak SNR, leading-edge slope and trailingedge slope are 3.22 m/s, 3.67 m/s and 3.49 m/s without data quality control and data calibration. When SNR is controlled to be larger than −6 dB, the RMSE of the retrieved wind speed using the DDM peak SNR reduces to be within 2 m/s, and the useful data are 78.11 % of that without data quality control. For leading-edge slope and trailing-edge slope, when SNR threshold is −3 dB, the RMSE of retrieved wind speed is also decreased to be within 2 m/s, and the useful data reduce to 53.74 %. Conclusions: The results show that the GNSS-R data from “Jilin-1” Kuanfu-01B satellite can be used to measure sea surface wind speed. The data used in the paper are uncalibrated, therefore, the retrieved performances are not optimal. It is a key work to calibrate the influence of transmitted power, transmission gain, and propagation path on the DDM. [ABSTRACT FROM AUTHOR]

Objectives: The seasonal subsidence and uplift of the surface elevation in permafrost area occur due to the annual melting and freezing of active layer, which has an important impact on the safety of engineering construction, the balance of ecological environment, and global climate change. Using global navigation satellite system-interferometric reflectometry (GNSS-IR) to monitor frozen soil deformation is a new technique. Aiming at the terrain influence caused by the non-daily-repeatable orbits of GLONASS and Galileo satellites, this paper proposes a new method to calculate the freeze-thaw deformation of permafrost using these GNSS data. Methods: By introducing the inclination angle of reflector surface, the effects of terrain changes are eliminated, and the seasonal freeze-thaw deformation closer to the actual situation is obtained. The GNSS signal-to-noise ratio data of 2018 and 2019 snow-free days at SG27 site in northern Alaska are used for experiments and compared with the results obtained by existing retrieval methods to verify the effectiveness of this method in monitoring permafrost deformation. Results: The results show that compared with the methods without considering the terrain effect, the surface elevation changes obtained by GLONASS and Galileo have smaller discreteness and smaller uncertainty, and the fitting consistency and determination coefficient R2 of the composite model have been improved. The total average standard deviations are reduced by about 28.9 % and 36.9 %, and the total average R2 are increased by about 0.23 and 0.24, respectively. The total average increases of daily data utilization rate of non-repetitive orbits are about 19.6 % and 22.8 % . Conclusions: This study provides a valuable reference for monitoring permafrost freeze-thaw deformation, and expands the application of GNSS-IR in GNSS system for permafrost freeze-thaw monitoring. [ABSTRACT FROM AUTHOR]

Objectives: The spaceborne global navigation satellite system-reflectometry (GNSS-R) technology has been extensively employed in monitoring surface hydrology in recent years. However, research on the key factor of precipitation in the surface hydrological cycle, especially its spatiotemporal relationship with cyclone global navigation satellite system (CYGNSS) signals, remains limited. Therefore, this paper conducts a detailed and comprehensive analysis of the direct relationship between CYGNSS reflectivity and precipitation, taking into account multiple influencing factors and scale effects. Methods: This study utilizes monthly CYGNSS data and precipitation data from 2019 to 2020, taking Guangdong Province, China as the study area. It analyzes the spatiotemporal response relationship between CYGNSS signals and precipitation, while comprehensively considering the influence of different driving factors such as digital elevation model (DEM), slope, land cover type, soil type, and normalized difference vegetation index (NDVI) on the CYGNSS signal-precipitation response. The study also explores scale effects, and uses a factor detector model in the geographical detector to investigate the degree of influence of each driving factor. Results: From 2019 to 2020, the monthly average CYGNSS reflectivity and precipitation show significant seasonal differences and nearly synchronous variations, validating the potential of CYGNSS signals for precipitation monitoring. The positive correlation between CYGNSS signals and precipitation strengthens with increasing spatial scale. For example, at small scale (1 km), the positive correlation between CYGNSS signals and precipitation is 21.00% (significant at the 0.05 level), while at medium scale (3 km) and large scale (9 km), it reaches 26.84% and 40.36% respectively, highlighting the advantage of CYGNSS in monitoring precipitation at larger scales. Additionally, the study found that at different spatial scales, regions with lower DEM and slope (indicating relatively flat terrain), areas with land cover type of cropland, and regions with lower to moderate vegetation coverage exhibited a stronger positive correlation between CYGNSS signals and precipitation. However, the influence of soil type on the relationship between CYGNSS signals and precipitation response varied across different scales. The driving factors 􀆳 influence on the relationship between CYGNSS signals and precipitation is ranked as follows: DEM>NDVI>slope>land cover type>soil type, and their influence becomes stronger with increasing scale. Conclusions: This study elaborates on the response relationship between CYGNSS signals and regional precipitation, providing useful references for further utilizing GNSS-R technology to monitor precipitation and facilitating accurate assessment of surface hydrological changes. The results demonstrate that certain spatiotemporal response relationships exist between CYGNSS signals and precipitation across different scales, and various driving factors have certain influences on their response relationships. [ABSTRACT FROM AUTHOR]

Objective: Navigation context comprises observation environment and carrier activity. While global navigation satellite system (GNSS) can provide high-precision navigation, positioning, and timing services for users, users can also annotate the types of navigation scenarios they are in by distinguishing the quality and features of terminal observation values. Navigation context clustering is important for adaptive seamless navigation and location services, which can significantly promote context adaptability, configuration flexibility, and system robustness. Methods: In terms of current issues that the division of water/underwater context is simple and has insufficient granularity, the quality and characteristics of GNSS observation signals in different water depth layers are analyzed by collecting GNSS observations in different water regions. Results and Conclusions: The results show that the underwater navigation context has remarkable layering. Considering that the navigation context has inherent properties such as segmentation, merging, and connectivity, this paper subdivides the underwater navigation context into three layers: Submerged water (about less than 6.5 cm), shallow water (about greater than 6.5 cm and less than 8.5 cm), and deep water (about greater than 8.5 cm) through K-means clustering, and conducts navigation context classification experiments using various GNSS signal features. It reveals that the clustering accuracy rate is 90.4%, indicating the effectiveness of submerged-shallow-deep navigation context division. [ABSTRACT FROM AUTHOR]

Objectives: The high accuracy alignment requirements of particle accelerators present a great challenge to measurement techniques. Due to the advantages of large volume, high precision and three-dimensional measurement of laser tracker, it is necessary to carry out the research on the measurement accuracy by laser tracker in particle accelerators. Methods: First, this paper studied the measurement accuracy of laser tracker itself, introduced the expression and test method of the accuracy, derived the calculation formula and explained its specific meaning of rated accuracy, which provided a theoretical basis for the measurement application and data processing. Then, the linear control network was taken as a representative for the accuracy analysis of control network by laser tracker in particle accelerator, and a measurement activity was implemented in the linac of China Spallation Neutron Source facility, two groups were assigned to survey with different laser trackers during the same period. Results: By analyzing the adjustment precision of each group and the coordinate deviations between the two groups, the weakest transversal accuracy of 200 m linear control network was confirmed and better than 0.2 mm. Moreover, in order to make a quick evaluation of the accuracy of linear control network, a method was proposed to estimate the accuracy with the network simplified to traverse network, and the rule of the weakest point accuracy was acquired that was proportional to the square root of the third power of length. Conclusions: These studies can provide guidance for accuracy analysis and scheme design of alignment in particle accelerators. [ABSTRACT FROM AUTHOR]

The “cloud control” paradigm, grounded on geocoded data such as digital orthophoto map (DOM) and digital elevation model (DEM), is gradually maturing for applications in medium and small-scale mapping. However, its essence of obtaining control points from the object space inherently carries errors within the DOM and DEM products, subsequently hindering its applicability in large-scale mapping scenarios. Constructing constraints based on original images with known accurate orientation parameters and adjusted object space tie points represents a theoretically superior cloud control paradigm. This paper proposes developing this image-based cloud control paradigm into a standardized survey and mapping product, termed digital control photo (DCP), aiming to better ensure positioning accuracy while reducing redundant labor and computational power consumption in revisit aerial survey tasks. This paper also introduces the application of DCP in calibration of large-format aerial cameras, geometric positioning of unmanned aerial vehicle images and satellite images, and suggests several recommendations for promoting the establishment of a DCP application system. [ABSTRACT FROM AUTHOR]

Continuous and dense global navigation satellite system (GNSS) observations of surface deformation can be used to recover regional terrestrial water storage (TWS) changes with high spatiotemporal resolution, which is of great significance for the study of regional water cycle and climate changes and has become a hot topic in the field of hydro-geodesy. This paper introduces the basic theory and inversion algorithms of the loading Green's function method and Slepian basis function method, and the stability and performance of these two methods in retrieving regional TWS changes by using GNSS surface deformations are evaluated. Meanwhile, the latest progress in using GNSS-derived TWS changes to study regional water cycles and extreme climate changes is reviewed. The existing problems and future development trends of the inversion of GNSS observations (e. g. refined GNSS data processing and joint inversion of multi-source data) are summarized and analyzed. It provides important insights into the use of GNSS observations to investigate regional TWS changes and related applications in hydro-geodesy. [ABSTRACT FROM AUTHOR]

The deformation response models of earthquake cycle play a crucial role in studying the entire dynamic process of earthquakes, from inception, nucleation and rupture, to postseismic adjustments, as well as in exploring the rheological properties of Earth' s crust and mantle. They have significant implications in earthquake prediction, forecasting, seismic hazard mitigation and risk assessment. First, this paper introduces the characteristics of deformation during the interseismic phase of the earthquake cycle. It categorizes and summarizes the corresponding models from the perspectives of dimensionality and elastic-visc-oelastic models, and it elaborates on the characteristics, limitations and historical evolution of simulation methods for coseismic deformation, with a particular focus on mainstream approaches for determining the geometry of coseismic faults and selecting smoothing schemes. Then, this paper discusses the spatiotemporal evolution features of postseismic deformation, and categorizes them according to deformation mechanisms, including poroelastic rebound, viscoelastic relaxation and postseismic afterslip. It also outlines the characteristics and limitations of simulation methods for deformation phenomena and provides a classification summary of current mainstream postseismic combined models. Furthermore, this paper explores the opportunities and challenges faced by earthquake cycle deformation simulation methods in the rapidly accumulating data environment. It emphasizes the importance of three-dimensional viscoelastic models, considering the viscoelastic relaxation effects of Earth's crust and mantle. Finally, this paper offers prospects for future research directions, including single-fault earthquake cycle deformation simulation, postseismic mechanisms, dynamic modeling and interpretation of possible earthquake cycle deformation phenomena. [ABSTRACT FROM AUTHOR]

Currently, spatiotemporal information, positioning and navigation have become important new infrastructures. Driven by general artificial intelligence, the era of intelligence led by large models has arrived. The increasingly powerful large models will play a more and more important role in the intelligent processing and applications of spatiotemporal information. First, this paper uses large models as the research paradigm and summarizes the current status and progress of large models in intelligent processing of spatiotemporal information in surveying and mapping. In addition, it analyzes the challenges faced by large models in intelligent processing of spatiotemporal information in surveying and mapping. We elaborate three key technologies in spatiotemporal information large models for surveying and mapping, including multi-modal fusion and understanding architecture design, prompt engineering optimization by fine-tuning, and human-in-theloop guidance for decision-making. Finally, the depth of industry understanding, data security risks, content credibility, and training deployment cost optimization are addressed to predict the development trend of spatiotemporal information large models. [ABSTRACT FROM AUTHOR]

High-precision global navigation satellite system (GNSS) positioning technology, as a hot spot in the field of satellite navigation, has expanded the in-depth development of the location-based service area, and has been widely used in smart cities, intelligent transportation, autonomous driving and other fields. First, this paper presents the current status and development of GNSS positioning technologies.Then, it explains the common basic theories such as integer ambiguity fixation, functional model and stochastic model fusion involved in multi-frequency multi-GNSS high-precision positioning, as well as the key techniques for improving GNSS positioning performance such as the non-model error processing, the cycle slip detection and data interruption repair. Based on the innovations of basic theories and key techniques, this paper introduces four new technologies for multi-frequency multi-GNSS high-precision positioning applications developed by Tongji University GNSS team in recent years. Finally, we remark the future development trend and challenges of GNSS-based high-precision positioning. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), as an important component of global water and energy cycles, plays an essential role in the Earth's water cycle, energy balance, and climate change. Traditional field measurement methods for SM are difficult to obtain the spatial distribution of large-scale and long-term SM and have high measurement costs. Microwave remote sensing, with its all-weather and penetrating capabilities, is one of the most effective means of obtaining soil moisture at a large scale and long-term manner, particularly the L-band global navigation satellite system-reflectometry (GNSS-R) technology with advantages of both active and passive microwave remote sensing, which has been paying a lot of attention in SM measurements. Based on the investigation and analysis of domestic and international progress, this paper systematically introduces the principle and characteristics of GNSS-R technology for obtaining SM, and gives the specific steps of SM measurement from both forward and backward retrieval aspects, the main faced difficulties, and key issues to be solved. Taking the latest cyclone GNSS (CYGNSS) satellite estimation as the main part, the theoretical model and method based on CYGNSS SM estimation are deeply expounded. In addition, the opportunities and challenges in the development of GNSS-R SM remote sensing are briefly analyzed, aiming to provide a reference for future ground-based, airborne, and satellite-based GNSS-R SM remote sensing observation and application in China. [ABSTRACT FROM AUTHOR]

The rise of geographic big data and the advancements in deep learning have brought new opportunities to solve challenges in geospatial analysis. Current methods are rooted in statistical correlations. However, the deceptive and misleading nature of correlation patterns, coupled with inherent biases in the data, make it difficult for those methods to obtain reliable analysis results. And the source of that difficulty is the fact that correlation in data does not imply causation. Serving as the key to unveiling unknown facts and discovering general underlying patterns, causation is critical to geographical research. Given the inherent complexity of earth system and that geographic researches are mainly based on observation into earth, it is impractical to conduct randomized double-blind experiments and inappropriate to directly apply the philosophical definition of causation in geographical researches. In light of that, this paper aims to provide fundamental principles and methodological support for geospatial causal analysis. On the basis of assumptions about geospatial causation, the principle of geospatial causation is proposed. Furthermore, the relationship between this principle and the spatial effects in geospatial analysis is elucidated, major pathways for causal discovery under the consideration of each spatial effect are also presented. [ABSTRACT FROM AUTHOR]

Based on the application in the field of photogrammetry, this paper retrospects the binocular image dense matching method. First, the two categories of local dense image matching and global dense image matching methods are concisely compared, and the advantages, disadvantages, and main challenges of each method are pointed out. Then, the building occlusion problem that is difficult to deal with in dense matching is analyzed. The common building occlusion phenomenon in aerial photography is divided into five types, and the pertinence of the existing occlusion detection and filling algorithms is expounded, which provides technical ideas for solving the bottleneck problem in dense image matching. Finally, the development trend of dense image matching of binocular images is prospected. This paper can help readers fully understand the traditional binocular image dense matching technology, and it will be beneficial to the research of dense image matching based on deep learning. [ABSTRACT FROM AUTHOR]

Objectives: The deformation measurement of long-span bridge alignment is of great significance to the assessment of bridge health and safety. The traditional deformation measurement methods of longspan bridge alignment, such as sensor and close range vision measurement, have shortcomings such as high measurement cost and limited measurement frequency, which are difficult to meet the requirements of highprecision measurement of deformation of long-span bridge alignment. Methods: Combined with dynamic precision engineering measurement and machine vision technology, this paper proposes a method for measuring the deformation of long-span bridge alignment based on inertial cameras. The camera is used to take high-frequency images of bridge measurement targets. At the same time, the inertial sensors are used to correct the measurement errors caused by camera movement. The deformation of long-span bridge alignment is calculated through inertial cameras in series mode, and the corresponding measuring equipment is developed and applied to the health monitoring system of a super large bridge. Results: The precision experiment results show that root mean square error of the proposed method is 0.38 mm. In engineering applications, the deformation of bridge alignment trend measured by the inertial camera is highly consistent with the static leveling results. The correlation coefficient is better than 99.90%, and the mutual difference is 4.94 mm. Conclusions: The proposed method can measure the linear deformation of large span bridges with high accuracy and real-time, and has good engineering value. [ABSTRACT FROM AUTHOR]

Objectives: Complex construction organization strategies such as parallel same direction and reverse boring for long railroad tunnel projects lead to sparse spatiotemporal distribution and large spatiotemporal overlap in the digital twin three-dimensional model. The existing spatiotemporal indexes for the continuous uniform spatiotemporal distribution characteristics can not satisfy the needs of efficient retrieval of time, mileage, and semantics in multi-dimensions, and they also restrict the efficiency of the digital twin application in real-time virtual and real mutual feeds. Methods: This paper proposes a multi-dimensional efficient spatiotemporal indexing method applicable to the digital twin model of railroad tunnels. First, a 3D tile data structure of railroad tunnels associated with time, mileage, and semantic multi-dimensions is designed. Second, an oriented bounding box corrective mechanism constrained by the global optimal solution is established, and then a multi-granular spatiotemporal index of railroad tunnel lines-tiles-components is realized. Finally, the effectiveness of the method is verified using typical long railroad tunnel twin model data. Results: The results show that the average value of the proposed method in the mixed retrieval time of time, mileage and semantic multi-dimensions is 135.44 ms, which improves the retrieval efficiency of the 3D spatial single dimension and the time-mileage mixed dimension comparing with the existing typical spatial and temporal index. Conclusions: The proposed method can effectively support the efficient spatial and temporal retrieval of the digital twin 3D model of railroad tunnels, and the application of high-performance visualization and analysis. [ABSTRACT FROM AUTHOR]

Objectives: Digital surface model is of great significance in the fields of real-life 3D modeling, smart city construction, natural resources management, geoscience research, and hydrology and water resources management. However, dense matching, as a core step in generating digital surface models, is prone to matching failures in regions with a lack of texture, disparity gap and inconsistent illumination. The sparse point cloud data with high accuracy and extensive coverage after aerial triangulation, which can be used as a priori information to improve the accuracy of dense matching results. Methods: First, this paper proposes a sparse point cloud guidance (SPCG) method for generating digital surface models of aerial images. The method aims to constrain the dense matching of images using sparse point cloud encrypted by aerial triangulation. The sparse point cloud guidance first selects stereo image pairs with good geometric configurations, high overlap, and extensive coverage. Then, the number of sparse points is extended by using the closest proximity clustering and pyramid propagation methods. Additionally, the matching cost of the extended points is optimized by using the improved Gaussian function to enhance the accuracy of the dense matching results. Finally, the sparse point cloud is fused with the dense matching point cloud to generate the digital surface model. Results: Experiments on simulated stereo images and real aerial stereo images show that the optimized semi-global matching by the SPCG method in this paper significantly improves the matching accuracy of the original semi-global matching algorithm and outperforms the semi-global matching optimized by the Gaussian method and the deep learning method, pyramid stereo matching network. Numerically, the percentages of disparity maps generated by semi-global matching with greater than 1, 2, or 3 pixels difference from the true disparities are 46.72%, 32.83%, or 27.32%, respectively, whereas the SPCG method decreases by 7.67%, 9.75%, or 10.28%, respectively, compared to the former. The experimental results of the multiview aerial images show that the SPCG method accurately generates the digital surface model of the whole survey area, and it is better than the digital surface model generated by the superior SURE software in both qualitative and quantitative aspects. Conclusions: Compared to the original dense matching, sparse point cloud-guided dense matching improves the matching accuracy in difficult matching regions such as weak textures, repetitive textures and depth discontinuities. In turn, high precision and high density point clouds are generated. A complete digital surface model is generated by the fusion of the densely matched point clouds. [ABSTRACT FROM AUTHOR]

Objectives: Ground-based synthetic aperture radar (SAR) time series analysis mainly selects coherent pixels with strong scattering characteristics and stable phase from SAR images for subsequent time series inversion. Coherent pixels between different interferograms are variable, which makes time series analysis complicated. Therefore, it is crucial to select a high number of coherent pixels that are of highquality to ensure precision in ground-based SAR deformation monitoring. Methods: To improve the reliability of time series analysis in ground-based SAR, this paper proposed the enhanced nonlocal coherent pixels selection (E-NonPS) method to select effective coherent pixels from the interferogram network, including full-rank coherent pixels and partially coherent pixels. Full-rank coherent pixels are selected by the nonlocal full-rank method, and partially coherent pixels are selected by setting the signal-to-noise ratio (SNR) threshold for pixels with coherence occurrences of more than N/2 in single look complex images. Results: We used E-NonPS method to test two ground-based SAR datasets to verify the effectiveness in different monitoring environments (e.g., high and low coherence areas), and compared the selection results of coherent pixels with three traditional methods, including amplitude dispersion index (ADI) method, coherence coefficient (Coh) method and nonlocal full-rank coherent pixel selection (NonPS) method. The experimental results show that E-NonPS method can significantly increase the number of selected coherent pixels, and effectively improve the accuracy, especially for low coherence areas. Conclusions: The propoesed E-NonPS method can effectively address the demands for selecting coherent pixels and ensure sufficient coherent pixels in time series analysis. [ABSTRACT FROM AUTHOR]

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