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Objectives: Millimeter wave radar has been widely used in automotive industry and other fields, but its application is mainly limited to the environmental perception of obstacles or specific tasks. At present, there is little research on the application of millimeter wave radar in the field of navigation and positioning. Methods: This paper first studies the raw data processing principle of millimeter wave radar, and then designs an indoor ego localization method which only depends on a low‐cost millimeter wave radar. The process mainly includes extracting centroid feature points using density based spatial clustering of applications with noise (DBSCAN) algorithm, matching centroid feature point pairs through nearest neighbor criterion, constructing nonlinear optimization function and solving positioning results using levenberg marquardt method. Results and Conclusions: Experiments show that indoor navigation and positioning can be solved in real time by using a low‐cost millimeter wave radar. Under static conditions, the average horizontal positioning accuracy can reach sub centimeter level (mean value is 0.82 cm and standard deviation is 0.47 cm). Under dynamic conditions, the absolute trajectory error can reach 0.66 m and the average heading angle error can reach 4.58°, which shows the feasibility of ego‐localization of low‐cost millimeter wave radar. Finally, this paper discusses the problems and feasible research ideas of low cost millimeter wave radar in navigation and positioning. [ABSTRACT FROM AUTHOR]

The Yangtze River Basin is an important part of China's ecology and social economy, and the land use/cover status is experiencing rapid changes. As the development of remote sensing technology, the Internet of things and social media, we can obtain huge, multi-dimensional relevant data of land use/cover, the process of land use/cover, driving factors and ecological response of research, the development of the Yangtze river basin planning and ecological protection provides a decision-making basis. First, this paper reviews the current research on land use/cover change in the Yangtze river basin, and then expounds the re⁃ search methods and conclusions in terms of the driving mechanism and ecological effects of land use/cover change. Finally, this paper discusses the difficulties and challenges faced by existing researches from the differences of land use/cover products, regional driving factors, complexity of ecological environmental change process and comprehensive evaluation of ecological service value and the possible directions of future researches. [ABSTRACT FROM AUTHOR]

Objectives: Gauss-Markov model, the model with condition equations and Gauss-Helmert model are three classical adjustment models. Gauss-Helmert model can be seen as the combined form of the other two models. They are usually distinguished by the number of unknown parameters. However, this classification method cannot reflect the essential relationship between them. In addition, the connections between Gauss-Markov model and the conditional model has been intensively investigated, while that of Gauss-Helmert model has been ignored. Methods: Some equivalent objective functions of the adjust⁃ ment of the least-square in the context of Gauss-Markov model and the conditional model are proposed based on the projection theory and the dual optimization technique. Based on the theory of matrix analysis, this paper realizes the conversion between Gauss-Markov model and Gauss-Helmert model according to partial orthogonality of coefficient matrices, thus completing the structure of these models and revealing the mathematical relationships. Results: The least-square adjustment within Gauss-Markov model and the model with condition equations are explained geometrically by employing the linear space and projector. The equivalence of the objective functions of these two models are also expounded. Combining the Pythagorean theorem and duality theory, we extend them to four equivalent objective functions and illustrate the construction principle. The transformation between Gauss-Markov model and Gauss-Helmert model are achieved by the so-called partial-orthogonality. Conclusions: The geometric reasoning of the least-squares adjustment is analyzed once more. Two objection functions are newly proposed, and the geometric relation and connotation between objective functions are revealed. Gauss-Helmert model is not only the combined model in terms of the model appearance, these three adjustment models can actually be mutually trans⁃ formed. As a result, the understanding of the mathematical nature of adjustment models and the corresponding estimation in this paper has theoretical and practical value. [ABSTRACT FROM AUTHOR]

Objectives: In view of the low accuracy of flow forecast in tidal reach due to the combined influence of runoff and tide, combining the ability of variational mode decomposition (VMD) to process non-stationary signals and the advantages of back propagation (BP) neural network to deal with nonlinear problems, this paper proposes a new method for discharge prediction in tidal reach based on the combination model of tidal current harmonic analysis and VMD-BP neural network. Methods: First, the harmonic analysis method is used to separate the tidal discharge and residual discharge from the original discharge. Second, a BP neural network is constructed based on error backpropagation algorithm. Then, the BP neural network is used to simulate and train the power flow data and residual current data processed by VMD. Finally, the output tidal discharge and residual discharge components are superimposed and reconstructed to obtain the final discharge prediction results. Results: Experiments performed at the Xuliujing section of the Yangtze Estuary show that the discharge prediction accuracy of BP neural network method is better than that of traditional harmonic analysis method, with the root mean square error (RMSE) is decreased by about 3 400 m³ /s, and the relative standard accuracy (RSD) is increased by about 6%. In addition, compared with the traditional method, RMSE of the proposed method in this paper is decreased by about 5 000 m³ /s, and the RSD is increased by about 9%. Conclusions: The combined model based on harmonic analysis and VMD-BP neural network can effectively improve the prediction accuracy of discharge in tidal reach. At the same time, it also provides a new idea for the discharge prediction in the waters with variable flow conditions. [ABSTRACT FROM AUTHOR]

Objectives: The Ripley' s K-function has been widely applied to test for spatial point patterns in many fields such as economic geography, plant geography, and epidemiology. Because of the mathematical confusion of spatial point processes, there are many errors in measuring the distributions of spatial point objects in the corresponding published papers. Methods: This paper discusses Monte Carlo simulation method in the application of Ripley's K-function from perspectives of dynamics of random point patterns, suitability of parameter definition, and reliability of edge correction algorithms. First, we introduce the estimation methods of spatial point pattern analysis based on Ripley's K-function. Second, the formula of Ripley's Kfunction is corrected by various edge effect correction methods and compared in the applications. Finally, the modified algorithm is employed for spatial point pattern analysis of the nutrient concentrations of orange plants in Xinping County, Yunnan Province, China. Results: The preliminary results indicated that the number of Monte Carlo simulations has a great influence on the analysis of the observed spatial point patterns. The significance tests of the observed spatial point patterns change in the process of running ArcGIS Ripley's K-function software package, showing some degree of uncertainty of the observed results. The edge effect correction of Ripley's K-function based on GIS techniques is greatly recommended due to its extension to an arbitrary polygon from a common rectangular shape. Conclusions: Considering the popularity of Monte Carlo simulation in statistics and spatial data analysis, this paper provides important insights on edge correction and significance tests in other spatial clustering methods. [ABSTRACT FROM AUTHOR]

High precision gravity observation is an important tool of seismological research and has played an important role in earthquake prediction in China in recent years. This paper first briefly introduces the development status and classification of gravity observation instruments, then compares the seismic gravity observations and researches for seismic events between China and Japan from the aspects of the scale of the seismic gravity observation network, the gravity data processing process and the interpretation of gravity observation results, and finally systematically analyzes the challenges faced by the gravity observation re⁃ search in China. The aim of this paper is to provide some reference and enlightenment for the follow-up work. [ABSTRACT FROM AUTHOR]

In the past two decades, satellite gravimetry has been widely developed to map the mass distribution on the Earth system. It helps human beings to have a deeper understanding of the dynamic processes that occur on the Earth's surface (e.g., atmosphere, hydrosphere, ocean, and polar ice sheets). This paper reviews the development history of challenging minisatellite payload, gravity recovery and climate experiment, gravity field and steady-state ocean circulation explorer, and gravity recovery and climate experiment follow-on. We briefly describe the research progress and problems of these gravity satellite missions. In order to overcome the shortcomings of the gravity satellites mentioned above, international research institutions propose numerous plans and simulation analyses for the next generation gravity mission (NGGM). This paper sorts out the task concepts, expected accuracy, and status for 12 kinds of NGGM proposed by international agencies. In order to introduce NGGM more clearly and grasp its current progress, we divide NGGM into four categories according to constellation configuration and satellite payload technology, including Sharifi-type gravity satellite constellation, Bender-type gravity satellite constellation, Satellitelink gravity satellite constellation, and Cold-atom gravity satellite constellation. This paper aims to sort out and summarize the NGGM to grasp the progress at this stage and presents some future perspectives for NGGM. [ABSTRACT FROM AUTHOR]

Objectives: Differential interferometric synthetic aperture radar (D-InSAR) has its own unique monitoring methods in acquiring surface deformation. In view of the difficulties in the application of D-InSAR technology in three-dimensional (3D) deformation monitoring of coal mine surface, this paper proposes a dynamic monitoring method based on fusion of single sight D-InSAR and Boltzmann-Knothe (BK) model. Methods: First, taking into account the characteristics of D-InSAR that it is difficult to obtain large gradient deformations, the Boltzmann function model with good boundary fit was selected, and BK model was constructed by the fusion of the Knothe time function. Second, due to the decoherence of the time-space baseline and the limitation of D-InSAR principle, it can often only obtain the short-term, one-dimensional deformation of the surface in line of sight (LOS). Therefore, based on the projection relationship between the 3D deformation and the LOS deformation, combined with the geological mining conditions of the target area, the fitness function with non-cumulative deformation was constructed. Finally, a dynamic monitoring method of surface 3D deformation of coal mine based on fusion of single sight D-InSAR and BK model was proposed by introducing the fireworks algorithm (FWA). Results and Conclusions: Simulation experiments show that the proposed method can reliably and accurately inverted all dynamic prediction parameters of mining subsidence, and the relative error of the solved parameters was between 0.11% and 7.51%. The subsidence and horizontal movement monitored by this proposed method were consistent with the real values, and the monitoring effect of the PIM-Knothe method was distorted. The results of the anti-error experiment show that under the influence of observation error or model parameter error, the overall effect of the solved parameters by the method in this paper was better, and the ability to resist errors was shown to a certain extent. The proposed method was applied to the 13121 working face of Gubei Mine in Huainan, and the surface 3D deformation of the coal mine from 2019-10-25 to 2019-12-12 was obtained. The conversion of short-term D-InSAR observation to 3D deformation dynamic monitoring based on the proposed method was realized, and the monitoring results were more consistent with the surface movement and deformation measured by the observation station. The reliability and scientificity of the dynamic prediction method for solving the surface 3D deformation of coal seam based on D-InSAR technology has been verified. [ABSTRACT FROM AUTHOR]

Objectives: The spatial distribution of rural settlements has unique regularity and complexity. In order to reduce the difficulty of cartographic generalization, we can cluster rural settlements first. Voronoi diagram has significant advantages in expressing the distribution characteristics of settlements, but now spatial clustering seldom considers direction relation between entities. Direction relation is an important part of spatial relation. In theory, the introduction of direction relation in spatial clustering can help to improve the clustering effect. Therefore, based on Voronoi diagram, this paper proposes a clustering method of rural settlement considering direction relation. Methods: First, Voronoi diagrams with distance constraint(DCVoronoi) are constructed, and Voronoi proximity relations among settlement entities are determined. Second, undirected features are calculated based on the area of entities and the area of DC-Voronoi polygons. Directed features are calculated based on offset direction and offset distance of entities in DC-Voronoi polygons. Aggregation strength values of all entity pairs are calculated by combining undirected features and directed features. Finally, clustering result is obtained by eliminating the proximity of entity pairs whose clustering strength value is less than the threshold. Taken the data of rural settlements in Ningbo as an example, this paper sets silhouette coefficient as result evaluation index. Results and Conclusions: Compared with the clustering results of density-based spatial clustering of applications with noise method and clustering by fast search and find of density peaks method, the results show that the proposed method can effectively cluster rural settlements with different distribution patterns, and can accurately identify the boundary of rural settlements. The clustering granularity is moderate, and the clustering results accord with people's cognitive habits. [ABSTRACT FROM AUTHOR]

Objectives: To address the problem of internal inconsistency of classification targets in existing three dimensional(3D) point cloud data segmentation and classification methods. we propose a high-precision classification method for indoor point cloud jointly optimized by super voxel random forest and long short-term memory (LSTM) neural network. Methods: The method takes into account that the super voxel structure has the characteristics of internal feature consistency, divides the original point cloud into super voxels, and uses super voxels as the basic unit for multivariate feature calculation to build a super voxel random forest classification model for indoor point cloud to achieve coarse classification of point cloud data. On this basis, LSTM is introduced to train and predict the neural network model for the hyper voxel neighborhood connectivity of coarse classification to achieve the optimization of hyper voxel coarse classification results. The validity and accuracy of the proposed classification method are verified based on the open dataset. Results: The results show that the classification accuracy of the proposed classification method can reach 83.2% for 13 types of elements in the open dataset. The training data of the LSTM optimization network proposed in this paper used only the label information of region 1 for model training, while other deep learning frameworks used regions 1-5 for model training, so from the perspective of training data requirements, the point cloud data classification framework proposed in this paper can achieve a relatively better prediction result with a small portion of the training data set. The super voxel-based LSTM optimization method approach has high classification accuracy on objects with obvious set features such as ceiling, floor and wall, however, it is inferior to the deep learning algorithm RandLA-Net in classifying objects with complex structures such as chair, sofa and bookcase. Conclusions: In this paper, we consider the association characteristics between different types of elements embedded in the connection relations among super voxels, and introduce LSTM to train and predict the model for the coarse classification of super voxel neighborhood connection relations to achieve the optimization of coarse classification results of super voxels. The proposed method can achieve better classification accuracy when trained with small samples compared with the classical deep learning framework. [ABSTRACT FROM AUTHOR]

The digital twin city seeks to restore virtually the geographic scene that is close to reality and provide the audience an immersive experience. Three dimensional (3D) real urban scene is one of the key technologies to achieve the goal. Nowadays, the combination of geographic information system and game engine becomes an industrial trend and provides new opportunities for 3D real urban scene. Oblique photogrammetric data, as being a fundamental data source for 3D urban modeling, is able to represent large-scale and high-precision scene. The game engine plays an excellent role in offering realistic effects and post-production systems that are greatly useful for geospatial data visualization. However, the diversity of specifications and formats of oblique photogrammetric data has caused many different data loading and transformation methods, resulting in a problem to be solved in 3D real urban scene. Methods: This paper aims to propose a data transformation approach from oblique photogrammetric data to rendering resources in game engine. To stress, the proposed approach can allow us to load instantly massive amount of oblique photogrammetric data in Unreal Engine, which is one of the most popular game engines. We further compared oblique photogrammetric data with rendering resources in game engine and discussed the significance of such a data transformation. The approach reorganizes the original structure of the data by parsing data into a tree structure that better suits transformation purposes. Also, the proposed approach transforms respectively the geometry and texture data when iterating tree nodes, as well as considers the essential physical properties of rendering assets. All these features are combined into a package data format. Results: In the case study of Futian district, Shenzhen, the results show that the proposed approach successfully converts oblique photogrammetric data to rendering resources in Unreal Engine 4. In the transformation process, the geometrical structure and collision properties of triangular mesh are guaranteed, and color, bump and other visual elements of original data are kept in materials. The rendering resources are loaded to simulate the real urban scenarios at different time periods of a day in Unreal Engine 4. Conclusions: This paper devises a novel approach which can convert the oblique photogrammetric data into rendering resource for a game engine to load and render, and meantime maintain the consistency of data's geometry and texture. For its scalability, it also supports parallel processing of large-scale data transformation when data is organized in the same directory. The proposed approach may be of great theoretical and practical value for other related 3D model data transformation problems. [ABSTRACT FROM AUTHOR]

The nonlinear remote sensing ecological index (nRSEI) is a recently proposed eco⁃ logical index, which used the kernel principal component analysis (kPCA) algorithm to integrate four indica⁃ tors of the existing remote sensing ecological index (RSEI) rather than using the traditional principal compo⁃ nent analysis (PCA) technique. The main reason for using kPCA was that in the Beijing area the correla⁃ tions between wetness, greenness, dryness, and heat that are four indicators used in RSEI were generally weak, so the kPCA that is specially developed to deal with variables with nonlinear relationship was needed to handle these four non-linear indicators. This paper aims to examine the correlation strength of these four indicators in the Beijing area to see whether their relationship is strong or weak and analyzes the effective⁃ ness of the accuracy assessment method that was used for the validation of the new index. Methods: Through examining correlation coefficients and scatter diagrams, the correlation between the four indicators is investigated to find out whether the relationship between the indicators is linear or non-linear. Also, the effectiveness of the validation for the new index is analyzed. Results: The results show that the four indica⁃ tors are strongly linearly correlated with each other, therefore, the kPCA was not suitable for the intergra⁃ dation of the four indicators. The methods used for the accuracy assessment of the new index also have obvious defects and thus failed to effectively validate the accuracy of the new index. In addition, some important issues related to prepare remote sensing papers are also discussed. These include the feasibility of remote sensing modeling, the robustness of the model, the scale consistency of the sub-indicators in the model, the validation method for the model, the selection of reference images for the validation, and the required sample size for accuracy validation. Conclusions: The nRSEI is not a suitable index for the assessment of regional ecological status as it mistakenly employed kPCA to intergrade the four indicators that are linearly related. [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]

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: 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]

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]

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]

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]

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]

Due to the blockage of satellite signals by the buildings and other structures, the high-accuracy, reliability and real-time indoor location-based services is one of the challenges for BeiDou navigation satellite system. As one of the core technologies of the spatio-temporal digital foundation for future artificial intelligence and super-intelligence applications, high-precision indoor positioning has long been a research hotspot in industry and academia. This paper first reviews the development status of existing high-precision indoor positioning technologies, and compares their advantages, disadvantages and difficulties. Then it analyzes the development trend of related technologies under typical applications, and looks forward to the fu ture development direction and application scenarios of indoor positioning technologies. [ABSTRACT FROM AUTHOR]

Objectives: The Turkey-Syria earthquake occurred on February 6th, 2023, and caused heavy casualties and property losses. This paper used high-resolution night-time light images to assess the damage of the affected cities. Methods: A multivariate power function regression model was used for intercalibration between sustainable development goals satellite-1(SDGSAT-1) and Yangwang-1 night-time light images. And the disaster assessment was carried out in the cities of Adiyaman and Antakya-Defne in Turkey. Results: The results show that the power facilities of Adiyaman and Antakya-Defne were severely damaged in the earthquake. The light loss rate of more than half of the building area exceeded 80%, and the overall light loss rate of the building area exceeded 70%. Additionally, the light loss rate in the east of Adiyaman was higher than that in the west, and the light loss rate in the downtown area of Antakya-Defne was higher than that in the suburbs. Conclusions: The high-resolution night-time light images can effectively assess the impact caused by natural disasters, and the assessment results can also help local government departments guide emergency relief and reconstruction work after the earthquake. [ABSTRACT FROM AUTHOR]

The modernization of territory spatial governance plays an essential role in realizing Chinese modernization. Towards the goal of high-quality development, promoting subject systems and professional learning provides fundamental and strategic support for the modernization of territory spatial governance. This paper focuses on the construction of the subject system of territory spatial governance from three aspects of the subject framework, the professional learning mode, and the team establishment strategy. We propose a life-cycle strategy for establishing the subject system of territory spatial governance in terms of the unified survey, spatial planning, utilization controlling, approval supervision, and construction of laws and regulations. We build a systematic talent training mode with the goal of generous theoretical foundation, method synthesis and excellent practical ability, and design a multi-field team construction strategy for territory spatial governance. In order to guarantee the modernization of land and space governance, the future works should focus on the synergetic development of subject development, talent training and team building. [ABSTRACT FROM AUTHOR]

Volcano deformation monitoring is one of the most widely studied research topics by using interferometric synthetic aperture radar (InSAR). In the new open and big SAR data era, the development of data processing methods, and interdisciplinary integration have pushed volcanic study into a new stage. This paper reviews the studies of volcano monitoring and parameter inversion based on InSAR. First, we introduce the principle of InSAR, the advanced InSAR time series methods, and the main error sources. Then, we summarize the commonly used classical analytical and numerical models in volcanic deformation modeling. We select several cases to introduce the specific InSAR applications in studying volcanic tectonic dynamic mechanisms including magma transfer, eruption, degassing and hydrothermal activity. Finally, we summarize the current status of volcanic deformation monitoring in China and the future advances of InSAR volcano geodesy. [ABSTRACT FROM AUTHOR]

Big data has provided new opportunities for landslide research, but in-depth mining of landslide data is still limited due to complex data types, diverse semantic relationships, and unclear sharing mechanisms. And it is difficult to give full play to the advantages of big data in landslide research. In this paper, a landslide knowledge graph construction method for the field of engineering geology is proposed, which extracts, fuses and structures multi-source heterogeneous landslide knowledge to realize the inquiry, correlation and reasoning of landslide knowledge big data. The combination of top-down and bottom-up methods was used to divide the concept and ontology of landslides. A knowledge graph model layer was formed based on 10 categories of knowledge, including landslide field investigation, landslide evaluation, landslide type, landslide geomorphological characteristics, landslide morphological characteristics, landslide disaster information, states of activity of landslides, landslide genesis mechanism, landslide stability analysis method, and landslide prevention and control measures. The knowledge graph model layer includes concept layer, attribute layer, relationship layer, rule layer, and instance layer, and it is constructed by extracting landslide knowledge information from broad data sources, establishing semantic networks and integrating redundant knowledge. The Neo4j platform is used to store landslide knowledge, realize knowledge visualization and retrieval, and provide new ideas and methods for landslide mechanism research and disaster prevention and mitigation. The proposed construction method of landslide knowledge graph can be extended to the research of other types of disaster knowledge graph, and is linked with other disciplines to promote the deep intersection and integration of disciplines. [ABSTRACT FROM AUTHOR]

Objectives: It is of great importance to precisely acquire the spatial distribution and dynamic variations of plastic greenhouses for agricultural development planning, grain assessment, and ecological environment monitoring. High-resolution remote sensing images offer detailed information of plastic greenhouse shapes and boundaries, satisfies the requirements of precision agriculture, and have irreplaceable advantages in conducting precise surveys of plastic greenhouses in crucial regions. Nevertheless, the mapping model of plastic greenhouses meets the challenges including inadequate prior information and unbalance between mapping accuracy and model complexity. Methods: This paper introduces a semantic segmentation model for plastic greenhouse that integrates the advanced plastic greenhouse index (APGI) and high-resolution red green blue (RGB) images. The proposed model primarily comprises the APGI semantic branch, RGB semantic branch, and RGB detail branch, and it incorporates an attention mechanism to effectively merge reliable prior information from the APGI index and spatial detail information derived from high-resolution RGB images. Furthermore, a model training mechanism guided by boundary information is proposed to enhance the precise recognition capability of the proposed model for plastic greenhouse boundary pixels. Results: The study reveals that, within an attention-guided fusion framework, the medium-resolution APGI can significantly enhance the identification capabilities of plastic greenhouses in high-resolution images. The proposed model can not only improve the mapping accuracy of plastic greenhouses but also greatly simpl-ifie the parameter size and computational complexity of the semantic segmentation model. Conclusions: This study meets the demands for rapid and accurate extraction of plastic greenhouse in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Objectives: Traditional remote sensing-based air temperature estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Methods: Taking the Yangtze River Economic Zone as a typical research area, this paper introduces the geographically and temporally neural networks for high-precision temperature estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused to obtain the spatially continuous near-surface temperature. The model performance was evaluated by the site-based ten-fold cross-validation method. Results: The results show that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the root mean square error(RMSE) =1.899 ℃, mean absolute error (MAE) =1.310 ℃ and correlation coefficient (R) =0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112 ℃ and 0.378 ℃, respectively. Conclusions: The temperature mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for temperature estimation with high precision. [ABSTRACT FROM AUTHOR]

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Objectives: Aiming at the defects of the traditional random sample consensus (RANSAC) algorithm for complex roof segmentation, is paper proposes an optimal RANSAC method for segmentation of complex building roof planes. Methods: First, the normal vector of the point cloud is estimated based on the k-nearest neighbor of the point cloud, and the seed selection process is optimized by using the point normal equation to improve the effectiveness of the initial plane generation; Second, the weight function based on the point normal and its distance to the initial plane is used to suppress the false plane generation. And inner points of the plane are modified iteratively by the weight function to improve the correctness of the segmented plane. Finally, the patch competition method is used to optimize the segmentation results and achieve roof point cloud segmentation. Results: Segmentation experiments results of multiple groups of buildings show that the proposed method can effectively suppress false plane, and the precision, recall and overall accuracy of roof plane segmentation of complex buildings are 96.8%, 98.2% and 95.1% respectively. Conclusions: Compared with the traditional method, the proposed method has obvious advantages in the accuracy and time-consuming of point cloud segmentation results. [ABSTRACT FROM AUTHOR]

Objectives: Latest release of international terrestrial reference frame has been generated with an enhanced modeling of nonlinear station motions, while more investigations on the strategies applied for the terrestrial reference frame modeling nonlinear station motions still needed. Methods: A global GPS (global positioning system) network has been reprocessed, using the latest GNSS (global navigation satellite system) data processing models and an integrated strategy, and then accurate and consistent input data are obtained; the post-seismic deformation models of 13 CMONOC (crustal movement observation network of China) stations are established and used as the input data, and FREQ2016 based on the function model, and GFZ2016 and EOST2016 considering the environmental loading correction are realized; these GPS terrestrial reference frames with different models are compared in terms of the goodness for fit, velocities and seasonal signals, and then the optimal model is explored. Results: The results show that the weighted root mean square averages of the postfit residuals of FREQ2016 are smaller than those of GFZ2016 and of EOST2016; FREQ2016 shows more significant seasonal signals, especially for semi-annual signals; the horizontal velocity field differences of these three are almost negligible (<0.08 mm/a), and the large vertical velocity differences, for some stations, reach 1.3 mm/a; the reduction of FREQ2016 horizontal velocity formal errors is about 10%. Conclusions: This paper suggests that the strategy based on the function model is better than that of considering the environmental loading correction. [ABSTRACT FROM AUTHOR]

Objectives: Satellite mission planning is an important way to rationally allocate satellite resources and obtain effective data. In order to solve the problem of resource waste in the absence of user demand for Qimingxing-1(QMX-1), we proposed a semantic description driven autonomous mission planning method for QMX-1. Methods: By analyzing the characteristics of earth observation targets and their requirements for remote sensing data, we have construct a semantic description model for global tasks and form a global earth observation target database. Based on the description model, we design a satellite autonomous mission planning method. By building a mission planning model, we can achieve autonomous planning for high-value targets and maximize the value of satellite in orbit applications. Results and Conclusions: We verifyed the proposed model based on the QMX-1 and proved the feasibility of the method in this paper. The model is applicable to different types of remote sensing satellites and can play a certain value in the future remote sensing constellation real-time intelligent service applications. [ABSTRACT FROM AUTHOR]

Objectives: The low-orbit giant constellation is an important new infrastructure of the country, and it is also the frontier and hot spot of the current development of the aerospace field. Methods: In this paper, through the “cloud-edge-end” dynamic optimization collaboration of satellite clusters and ground resources, the key issues of cloud-edge-end collaborative intelligent service for low-orbit giant constellation are studied. Focusing on the goal of one satellite multi-purpose, star cluster networking, collaborative sensing, autonomous control, and intelligent service, we analyze the key technologies such as intelligent control and high-precision processing of ground center cloud, collaborative observation and fusion processing of space-based edge cloud, real-time processing and intelligent service of satellite and user. Results and Conclusions: Based on the characteristics of observation targets, we have formed a“cloud-edge-end”collaborative intelligent service model for real-time tasks, which can provide fast, accurate and flexible space-time continuous information service for typical real-time application scenarios, and realize the leapfrog development of intelligent earth observation. [ABSTRACT FROM AUTHOR]

Objectives: As one of the precursor features of slope deformation, surface cracks can provide forecast information for the early identification of geological hazards and the determination of motion instability characteristics. Affected by terrain conditions, the efficiency of manual investigation is low, and it is difficult to solve the problem of size effect and noise filtering of cracks in complex backgrounds using a single remote sensing data identification. In order to efficiently collect the location and geometry information of surface fractures on deformed slopes, the main purpose is to develop a method system for automatic crack identification and information statistics based on remote sensing data, which is suitable for large-scale slope surfaces. Methods: Using the high-resolution three⁃dimensional point cloud and orthophoto image generated by autonomous terrain-following flight technology as the data source, the automatic extraction of cracks and the statistical research on the deformed slope of the reservoir bank of Baihetan Hydropower Station are carried out. The initial automatic identification of slope cracks is completed by using six algorithm models with point cloud's roughness, slope, dispersion and digital image's pixel gradient, gray value and RGB (red green blue) value as identification features respectively. Completed receiver operating characteristic curve (ROC) tests of different models and determined segmentation thresholds. The initial extraction results are processed by morphological repair and three filtering algorithms of crack direction, length and frequency based on density-based spatial clustering of applications with noise (DBSCAN) algorithm index to deal with background noise. The two-category evaluation index is used to analyze the pros and cons of the 6 filtered crack extraction results, and for the effect of crack size, the optimal detection model after data fusion is obtained. Based on the crack skeleton and outline, 6 quantitative characteristic indicators of quantity, length, width, direction, dispersion, and crack density are automatically calculated. Results: Starting from the fracture image characteristics of different scales, 6 different fracture identification models are used and ROC test is carried out, and the area under the curve values are all between 0.6 and 0.85. The highest F1 value of the model after fusion is 0.835 0, which can better meet the actual engineering needs. The morphological repair and filtering algorithm proposed can effectively reduce the background noise of cracks, improve the overall accuracy of the model, and can remove up to 82.7% of the noise with minimal crack distortion. The density clustering algorithm of DBSCAN is used to complete the fracture pixel classification and quantity statistics, and according to the ratio of pixel size and actual distance, the algorithm automatically completes the information acquisition of fractures, which can provide technical support for quantitative description and evaluation of fracture characteristics. Various types of crack identification algorithms used in this paper have different advantages and disadvantages and applicable situations, and the optimal combination relationship can be selected according to the field conditions with different characteristics. Conclusions: In this study, a method for automatic crack extraction and information statistics is constructed in a large-scale, multi-scale complex deformation slope scene, which can meet the needs of actual production operations. At the same time, it has important practical significance to further promote the early identification of geological disasters and intelligent monitoring and early warning. [ABSTRACT FROM AUTHOR]

Objectives: Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in synthetic aperture radar (SAR) image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. Methods: The proposed method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Using this multi-resolution SAR image coastline segmentation dataset, we perform experimental analyses on the effectiveness of the proposed network and the contributions of each module. Results and Conclusions: Experimental results show that the proposed method can work well for the task of sealand segmentation, the overall classification accuracy and mean intersection over union achieve 98.21% and 96.47%, respectively. [ABSTRACT FROM AUTHOR]

Digital information technology has made human beings move into a mixed space where virtuality and reality are integrated. Maps, that have always been used as the abstraction and interpretation of the real world and assist us to cognize the real world, are facing new challenges. Under the new digital and communication situations, maps need to incorporated digital virtual spaces and analyze the impacts brought by virtuality and reality, nihility and existence and the interpromotion of void and solid. Game maps are suitable carrier in this paper to analyze and discover the relations between the virtuality and the reality. Game map has seven elements including the time, the place, the personage, the object, the event, the phenomenon and the scenario to describe the world. This paper analyzes their virtual and real characteristics, explores the relationships and the interactions between virtual world construction and the reality, and discusses their feedback to the real world. This research can enrich the theoretical system of cartography and facilitate planning a smarter and more pleasant society for us. [ABSTRACT FROM AUTHOR]

From the perspective of map cognition, this paper analyzes the important role of mental map in a successful event of family search. Firstly, several basic concepts of map cognition such as spatial cognition, mental map and cognition mapping are clarified, and the position and function of mental map in traditional cartographic triangle are expounded. A detailed empirical analysis is made on the cognitive process of both the seeker and the sought in this case. Three geospatial cognition problems in the interview are discussed: The adaptability difference of map tools to spatial cognition stage, the spatial scale and temporal characteris‐ tics of mental map matching, and the top-down influence of cognitive subjects' knowledge experience on cognitive results. Finally, this paper puts forward the development trend of geospatial cognition entering the new era under the promotion of brain science and artificial intelligence research. [ABSTRACT FROM AUTHOR]

With the advancement of information and communication technology (ICT), a large number of new visualization types and techniques of map representation have been developed, and the term of panmap is proposed due to these new cartography visualizations. However, the map design and production still greatly depend on map makers experiences and professional skill, which cannot meet new requirement in ICT era. In order to enhance the objective and automatic levels in the process of map design and production in information era, and keep developing of map theories with the new techniques of cartography visualization, this paper aims to develop the visualization mechanism of pan-maps representations. In this paper, the relationships between requirements of pan-map representations and visualization dimension types are constructed based on visualization dimension system and map functions. An analysis on the typical map visualization of different visualization dimension types has been conducted. The visualization mechanism of pan-maps has been constructed and follows the strategy “analysis of representation requirements → combination of visualization dimensions → determination of visualization representation types → map visualization”. This study is helpful to the developments of cartography research and applications, the popular map design, representation, and production. [ABSTRACT FROM AUTHOR]

Objectives: In the transferring process from the modern society to the post-modern society, information is transmitted via We Media, in which the maps that are made by public users and map enthusiasts is not at high accuracy, and the expressions of the maps are diversified. WeMap is a kind of public-made maps, aiming to improve the map expressions to avoid the problems generated due to the lack of spatial cognitive knowledge, and to focus on the expressions of the map users subjective information. Unlike the traditional maps, WeMap aims to express geographical features and phenomena from multiple per‐ spectives and dimensions. This paper examine the origins of the above characteristics from the postmodern philosophy perspective. Methods: Metaphoring maps and language, taking postmodern philosophical methodologies such as embodied cognitive view, prototype category theory and decentralism of cognitive linguistics as tools, this paper critically analyzes and reconstructs the fundamental problems including spatial cognition, information classification and cartographic expression of traditional maps, and forms the conceptual framework of WeMap with the characteristics of postmodern philosophy. Results: WeMap cognitive process model was proposed, consisting of two processes: Map making process which can be summarized as mental image-image schema-prototype category-geographic knowledge-WeMap, and the map reading process which can be described as visual representation-visual feature-semantic and spatial relationship-cognitive map-spatial analysis-spatial behavior. The cognitive hierarchy model of WeMaps may be classified into six levels ( from IS1 to IS6) by the image schema. IS1 is suitable for most of public users who own the basic body image schema and it corresponds to the lowest cognitive ability, while IS2, IS3, IS4, IS5 and IS6 correspond to more advanced cognition abilities. The information classification of WeMap coin‐cides with prototype category theory which can be realized according to the principle of achieving common ness with prototype and compatibility with category. WeMap advocates pluralism which embodies in fuzzy map projections, various forms of map symbols, and even the opposite generalization standards compare to traditional maps. Conclusions: WeMap is a new map form in the era of We Media whose cognitive model, classification principle and map expressions coincide with the ideas of embodied cognitive view, prototype category and decentralism of postmodern philosophy. The philosophical basis of traditional map is more inclined to metaphysical mechanical objectivism. Although it recognizes the role of human cognition in map making and use, the human here refers to map experts. Maps can only be developed under the design of map experts, which is difficult to give full play to the linguistic potential of maps. Map theories, such as adaptive maps and crowdsourcing maps, have begun to pay attention to the transformation of their philo‐sophical basis, emphasizing the transfer of the main body of cartography and map use from map experts to public users, but they are still limited by the design knowledge and ability of map experts. WeMap aims to put map experts and public users on an equal position, fully respect the individual geospatial cognitive needs and habits of each map user, the decentralization of WeMap conforms to the development trend of human post-modernist philosophy. [ABSTRACT FROM AUTHOR]

Firstly, this paper discusses the change in the way of thinking and working of the cartographers and cartographic engineering through data, big data and space-time big data, which promotes cartography to enter a new era of the “fourth scientific paradigm” characterized by “data-intensive computing”. Secondly, this paper analyzes the functions and features of the digital transformation results of 4D products, and the progress in diversification and individuation of the map products, and the “design - compilation-publication” integrated digital transformation of mapping, and the feature of the representative atlases published in 2021. Finally, by giving two specific examples, i.e., the intellectualization of map generalization and digital mapping, this paper reviews four evolutionary stages of map generalization in the era of artificial intelligence and summarizes the difficulties and breakthroughs in key technologies, and provides a profound conclusion of the research status and the issues to be further explored in the domain. [ABSTRACT FROM AUTHOR]

Nowadays, human society has entered the ternary spaces, where geographical space, human space and information space are integrated. Driven by new technologies and demands, the three basic elements of map spatial cognition, cognitive subject, object and expression, have shown generalized characteristics. The existing map spatial cognition theories and methods can no longer support the accurate cognitive expression of pan-maps. Therefore, this paper first summarizes the progress of map spatial cognition research at home and abroad and its limitations, including the extension of cognitive subject studies， visualization of information space, usability of novel pan-map representation. Then, we propose a panmap spatial cognition research framework, including experimental methodology, cognitive mechanism, and expression model. To the end, we hope that this paper can provide deep insights for future studies and pro‐ mote the establishment of related theories and methods that adapt to the development trend of pan-maps, so as to solve a series of problems brought by the continuous generalization of elements of map spatial cognition. [ABSTRACT FROM AUTHOR]

This paper discusses the cognitive connotation of deep mapping. It attempts to consider deep mapping as an interdisciplinary research field, a combination of neuroscience and cartography, and a con‐ nection between brain mapping and geographic mapping. Recent developments in neuroscience, specifically the discovery of spatial cells such as place cells and grid cells, have provided a neuroscientific foundation for studying deep mapping. This paper proposes a conceptual model of deep mapping to establish the relation‐ ship between spatial cell properties and map functions (such as map scale, map measurement, map projec‐ tion, and navigation), and to elucidate the neural mechanism of maps by analyzing an abundance of neuro‐ science literature. This study of deep mapping aims to increase understanding of the nature of maps, enrich cartographic theories, encourage the integration of cartography and artificial intelligence, and expand their applications. [ABSTRACT FROM AUTHOR]

Objectives: Due to the joint constraints of boundary conditions, including seabed topography, driven water level at open boundary (DWLOB) and bottom friction coefficient (BFC), the accuracy of the tidal numerical modeling in coastal and offshore waters is relatively low. Methods: This paper intends to synchronously optimize the multiple boundary conditions, including seabed topography, DWLOB, and BFC, to improve the accuracy of the tidal numerical modeling in China’s coastal and offshore waters for the hydrographic surveying and mapping. This paper simulates the tidal model of Haizhou Bay of the Yellow Sea in China, using a two-dimensional tide numerical model (2D-MIKE21) and based on the synchronously optimized boundary conditions. The water depth with higher resolution and accuracy than the charted depth is used as the seabed topography. The DWLOB is calculated from 12 tidal constituents of the regional tidal model of China seas(CST1). The calculation of the BFC takes into account the spatial variation of water depth. Results: For validation, we compare the simulated model with the 1-year tide tables from 6 tide gauges in Haizhou Bay and the CST1 model at 24 randomly selected points, and get the total root sum squares of the 12 tidal constituents of 5.52 cm and 7.10 cm, respectively. The simulated tide model and CST1 are also compared with the 1-month observations at two tide gauges in Haizhou Bay, and the former has a smaller mean square error than the latter. Conclusions: The proposed strategy provides a new method for tidal numerical modeling in coastal and offshore waters. This study also shows that it is feasible to obtain the astro-meteorological constituent Sa by tidal numerical modeling. It should be noted that, the wind effect is also not considered in this study due to its strong randomness and the difficulty in obtaining data for one year. We can use the simulated water level heights and the short-term wind velocity and direction as the better open boundary and initial conditions to carry out the short-term tidal modeling in coastal and offshore waters, which can generate the residual water level or storm surge. [ABSTRACT FROM AUTHOR]

The study of asperities is of great importance for seismic hazard analysis. This paper introduces the concepts of asperities, barriers and their source models, and reviews the progress of research on the determination of asperities by seismic geodesy, mainly discusses the technique and method for determining asperities from the aspects of medium structure analysis （high-intensity anomalies）, seismic activity analysis, the fault slip pattern, and the problems are analyzed. This paper also prospects the determination of the distribution of asperities on active faults, future researches will focus on joint inversion of geodetic data, joint geodetic and seismic geophysical data, and machine learning based methods to determinate the distribution of asperities. The study provides a strong theoretical basis for seismic safety evaluation. [ABSTRACT FROM AUTHOR]

Objectives: Incremental structure from motion（SfM） has become the widely used workflow for aerial triangulation（AT） of unmanned aerial vehicle （UAV） images. Recently, extensive research has been conducted to improve the efficiency, precision and scalability of SfM-based AT for UAV images. Meanwhile, deep learning-based methods have also been exploited for the geometry processing in the fields of photogrammetry and computer vision, which have been verified with large potential in the AT of UAV images. This paper aims to give a review of recent work in the SfM-based AT for UAV images. Methods:Firstly, the workflow of SfM-based AT is briefly presented in terms of feature matching and geometry solving,in which the former aims to obtain enough and accurate correspondences, and the latter attempts to solve unknown parameters. Secondly, literature review is given for feature matching and geometry solving. For feature matching, classical hand-crafted and recent learning-based methods are presented from the aspects of feature extraction, feature matching and outlier removal. For geometry solving, the principle of SfM-based AT is firstly given with some well-known and widely-used open-source SfM software. Efficiency improvement and large-scale processing are then summarized, which focus on improving the capability of SfM to process large-scale UAV images. Finally, further search is concluded from four aspects, including the change of data acquisition modes, the scalability for large-scale scenes, the development of communication and hardware, and the fusion of deep learning-based methods. Results: The review demonstrates that the existing research promotes the development of SfM-based AT towards the direction of high efficiency, high precision and high robustness, and also promotes the development of both commercial and open-source software packages. Conclusions: Considering the characteristics of UAV images, the efficiency, precision and robustness of SfMbased AT and its application need further improvement and exploitation. This paper could give an extensive conclusion and be a useful reference to the related researchers [ABSTRACT FROM AUTHOR]

Geographic Information System has made great progress in theory and technology in recent 60 years. The application field of GIS has expanded to all aspects of society, and the social influence is increas‐ able growing. The architecture, development mode and service mode of GIS have undergone profound changes. In order to promote the further development of GISystem, this paper focused on the following three issues based on summarizing the three meanings of “S” in “GIS”, namely “System” “Science” and “Service”, and the fruitful achievements of “GIS” in recent 60 years. The first issue is how to un‐ derstand GISystem. This issue should be analyzed from two aspects, and one is to discuss the connotation of geographic information system through analyzing the three key words (namely system, information and geographic) of GIS. The other one is to analyze the relationship between GIS and map, computer mapping and map database. This paper holds the idea that GIS originates from and goes beyond map, computer map‐ ping and map database. It should be considered that GIS has the characteristics of equipment. The second issue is how does GIS evolve. This issue focuses on the social demand background, technical background and discipline background of the development and evolution from “Geographic Information System” to “Geographic Information Service”. It also focuses on the main manifestations of the development and evo‐ lution of GIS from the aspects of application field, expansion of data resources and functions, architecture, development mode and service mode. The third issue is where will the future development of GIS go. We discussed that the limit application domain expansion of GIS and point out that the future development of GIS must face the urgent needs of national economic construction and national defense construction. Based on the analysis and comparison of three existing GISystem service modes, we consider that the “hybrid” technology system of “grid integration” and “elastic cloud” is the best choice for GISystem service mode. Finally, this paper puts forward six key technical problems that must be solved in the implementation of “hybrid” spatiotemporal big data platform technology based on “grid integration” and “elastic cloud”and designs the application mode of “spatiotemporal big data platform”. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric water vapor is one of the important components in troposphere. It is of great significance to study the factors and accuracy of influencing water vapor. This paper mainly studies the influencing factors of precipitable water vapor (PWV) in the Loess Plateau and evaluates its actual accuracy. Methods: Firstly, the pressure, temperature data of the fifth-generation atmospheric reanalysis data of EC-MWF (ERA5) and global navigation satellite system (GNSS) derived zenith troposphere delay (ZTD) are evaluated. Secondly, according to the pressure and temperature data of ERA5 and ZTD data of crustal movement observation network of China (CMONOC), the PWV with 1-hour time resolution is calculated, and the theoretical error of PWV is deduced by using error propagation theory, and then the accuracy of PWV in the Loess Plateau is analyzed by comparing with the actual calculation error of PWV. Results: The results show that the average root mean square(RMS) and bias of GNSS-derived ZTD calculated by GAMIT/GLOBK and PANDA are 4.05 mm and −0.46 mm, respectively. The average RMS and bias of ERA5-provided pressure and temperature are 3.36 hPa/1.97 K and − 0.01 hPa/0.04 K, respectively. The theoretical error of PWV in the Loess Plateau is 1.51 mm, and the actual error is 1.94 mm. Conclusions: The accuracy of PWV calculated in this paper is high, which is of great significance to the investigation of water vapor distribution and climate monitoring. [ABSTRACT FROM AUTHOR]

Objectives: The pollution of heavy metal has become increasingly serious in recent years. The accumulation of heavy metals in the soil will be a threat to ecological balance and human health. Therefore, we need to obtain heavy metal content in soil quickly and accurately. Methods: This paper proposes a method to combine field and laboratory spectra to construct a mechanism estimation model of soil lead (Pb). Firstly, direct standardization (DS) algorithm was employed to eliminate the influence of environmental factors on the field spectra. Secondly, in order to enhance the diversity of the samples, the laboratory spectra were introduced to joint modeling. Finally, the characteristic spectra of iron oxide were extracted for modeling to increase the model rationality. Results: This method was validated by the spectra of 70 soil samples from Xiong'an farming area in Hebei province. The accuracy R² of model established by full-band field spectra without DS correction was only 0.220 0. However, the accuracy R² of model established by the pro⁃ posed method in this paper reached 0.914 6.Conclusions: It indicates that the model for estimating Pb content can be significantly improved by removing the influence of environmental factors on the field spectra, extracting the iron oxide characteristic spectra of the combining field spectra with laboratory spectra. [ABSTRACT FROM AUTHOR]

Global navigation satellite system(GNSS)⁃acoustic seafloor positioning is a technology proposed and developed for deformation monitoring of plates in the submarine subduction zone. As an important technology for the construction of marine space time reference network, it has broad application prospects. Although the current research results of GNSS⁃acoustic seafloor positioning cannot meet the construction re⁃quirement of marine space time reference network, its data processing method, especially the fine processing method of sound speed error, has important reference significance for the construction of seafloor part (seafloor geodetic reference station) of marine space time reference network. This paper firstly introduces the origin of GNSS ⁃acoustic seafloor positioning and summarizes it into two categories: Static measurements and dynamic measurements. At the same time, the sound speed error processing method is used as the development line to refine three development stages of the technology: Only assuming the vertical stratification of ocean sound speed, considering the temporal variation of sound speed, and considering the horizontal gradient of sound speed. For the stage of only assuming vertical stratification of ocean sound speed, foreign scholars used geometric structure symmetry to weaken the influence of sound speed error; domestic scholars mainly studied the error sources besides the sound speed (lever arm offset, time tag deviation, attitude angle error, etc.) and used the stochastic model to weaken the influence of system error on the positioning results. For the stage of considering the time⁃domain variation of sound speed, foreign scholars used fitting method (polynomial fitting or cubic spline fitting) combined with parameter smoothing constraint to solve the time⁃domain variation of sound speed, and improve the stability of positioning results. Based on this, domestic scholars have refined the parameter fitting method (considering the variation characteristics of the long period term of parameters). In addition, domestic scholars have proposed the underwater differential positioning algorithm to innovate the seafloor positioning technology. For the stage of considering the sound speed horizontal gradient, scholars have solved the sound speed horizontal gradient parameters in the GNSS⁃acoustic seafloor positioning processing, which improves the stability of the horizontal positioning results, and verified the reliability of the results by using the marine numerical model.This paper presents the prospect of high precision data processing methods of GNSS acoustic seafloor positioning for the seafloor reference station construction, and introduces the concept of small spatial⁃temporal scale sound speed tomography (sound speed error processing method based on ocean positioning reference network) in order to solve the problem that numerical prediction model cannot provide small spatial⁃temporal scale products, so as to provide high⁃precision sound speed error correction services for underwater vehicles. [ABSTRACT FROM AUTHOR]

Objectives: Forest canopy density is an important factor in forest resource surveys. It plays an important role in forest quality evaluation and forest resource management. In recent years, artificial intelligence technology and remote sensing technology have developed rapidly and they have been successfully applied to forestry remote sensing quantitative estimation. It is significant to study how to use deep learning methods to effectively integrate the remote sensing data with different spatial coverage capabilities in regional forest canopy closure estimation. Methods: This paper proposes a deep learning model (UnetR) which aims to estimate forest canopy closure based on deep learning model with the high-density light detection and ranging and high spatial resolution satellite imagery. We optimized the loss function of Unet for image classification, and added a batch normalization layer after the convolution layer, the model had the ability to quantitatively estimate continuous variables. Results : The comparative evaluation results with fully convolutional networks, random forest and support vector regression models show that the root mean square error of the UnetR model was lower, the estimation accuracy was higher determination coefficient is 0.777, root mean square error is 0.137, estimation accuracy is 75.60%. Conclusions: This paper provided a low labor cost and high degree of automation estimation model for remote sensing monitoring of regional forest canopy closure. [ABSTRACT FROM AUTHOR]

Objectives: In the remote sensing (RS) big data era, intelligent interpretation of remote sensing images (RSI) is the key technology to mine the value of big RS data and promote several important applications. Traditional knowledge-driven RS interpretation methods, represented by expert systems, are highly interpretable, but generally show poor performance due to the interpretation knowledge being difficult to be completely and accurately expressed. With the development of deep learning in computer vision and other fields, it has gradually become the mainstream technology of RSI interpretation. However, the deep learning technique still has some fatal flaws in the RS field, such as poor interpretability and weak generalization ability. In order to overcome these problems, how to effectively combine knowledge inference and data learning has become an important research trend in the field of RS big data intelligent processing. Generally, knowledge inference relies on a strong domain knowledge base, but the research on RS knowledge graph (RS-KG) is very scarce and there is no available large-scale KG database for RSI interpretation now. Methods: To overcome the above considerations, this paper focuses on the construction and evolution of the RS-KG for RSI interpretation and establishes the RS-KG takes into account the RS imaging mechanism and geographic knowledge. Supported by KG in the RS field, this paper takes three typical RSI interpretation tasks, namely, zero-shot RSI scene classification, interpretable RSI semantic segmentation, and large-scale RSI scene graph generation, as examples, to discuss the performance of the novel generation RSI interpretation paradigm which couples KG and deep learning. Results and Conclusions: A large number of experimental results show that the combination of RS-KG inference and deep da‐ ta learning can effectively improve the performance of RSI interpretation. The introduction of RS-KG can effectively improve the interpretation accuracy, generalization ability, anti-interference ability, and interpretability of deep learning models. These advantages make RS-KG promising in the novel generation RSI interpretation paradigm. [ABSTRACT FROM AUTHOR]

Objectives: The complex and dangerous mountainous environment, uncertain geographical and geological conditions are the key factors affecting the safety, quality and schedule of railway tunnel construction. Methods: Orient to intelligent and precise construction management, this paper proposes a top-down and bottom-up combination of railway tunnel drill safety-quality-schedule knowledge graph construction method, clarifies the conceptual connotation and semantic relationship for the five key elements of man-machine-material-method-environment(4M1E) related to safety-quality-schedule during the construction of railway tunnels, and designs a two-way collaborative construction method of mode layer from top to bottom and data layer from bottom to top, then introduces key technologies such as data acquisition and knowledge extraction. Results: Taking the construction event of the Kangding No. 2 railway tunnel exit work area as an example, we construct a case knowledge graph. The results show that the knowledge graph constructed by the method in this paper finely depicts the key element attributes that affect safety‐quality‐schedule, the semantic relationship between the elements, and the mutual feedback relationship, etc. Conclusions: Our proposed method provides key support for the overall systemic intelligent management of safety-quality-schedule in the whole process of railway tunnel drilling and blasting construction, and also lays the foundation for the digital twin of railway tunnel engineering. [ABSTRACT FROM AUTHOR]