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This paper proposes a method of establishing a time reference without a free paper time scale. The core idea is predicting and steering each atomic clock versus external reference time scale, respectively.Each clock can obtain a time scale steered by the external reference time scale. Then, the time reference is a weighted average of the steered time scales. The new method avoids the problems resulting from the correlation of the paper time scale and the time difference of each contributing clocks of traditional methods. Moreover, prediction algorithms and steering algorithms can be optimized according to different clocks with different characteristics, respectively. At the beginning, this paper briefly describes the basic principles of traditional methods and their weaknesses. Then, the basic principle, the theoretical advantages and the design principles of the prediction algorithm, steering algorithm and weighting algorithm of the new method are described in detail. An experiment shows the weakness of traditional methods. A simulation shows the traditional method and new method performances, and also preliminarily validates the excellent performances of this method.

The aggregation of buildings is generally based on the disjoint in spatial topological relationships. However, with the diversification of data sources, the spatial topological relationships between polygons inside large buildings also become diversified and even inconsistent, making it difficult to aggregate building polygons, while the multi-scale visualization of buildings in the smart city requires rapid and accurate extraction of the contour shapes of complex buildings. For this reason, this paper proposes an aggregation method of buildings based on the shortest adjacent line between vertexes interpolated. First, recursively search for adjacent polygons based on the aggregation distance to determine the clustering regions, which is convenient for processing the map data in every region. Then, for each clustered region, encrypt vertices for each polygon using a linear interpolation method according to the order of the points on it; after that, search and construct the shortest adjacent line between each vertice and the adjacent polygon through the grid index, and delete the three types of shortest adjacent lines which are abnormal; then, merge the effective shortest adjacent lines in a certain order to form the shortest adjacent line group, and further filter out three kinds of special cases, so as to determine the aggregation area of adjacent polygons; finally, the recursive search is performed clockwise according to the minimum rotation angle, and the contour of polygons including the inner ring and the outer ring is extracted. Experiments show that this method can quickly detect the aggregation area between the adjacent polygons. Experiments in this paper show that this method can quickly detect the adjacent aggregation areas between building polygons and accurately extract the outer contour lines of complex building entities in real-time. The overall contours extracted are close to the actual situation, which provides conditions for building visualization.

In order to effectively solve the trust problem of both parties in the process of geographic data transaction, this paper proposes a geographic data transaction certificate and copyright protection model combining zero-watermark, IPFS and smart contract technology. The model uses the IPFS system to build a new mechanism for zero-watermark registration, which can eliminate the dependence on third-party IPR institutions. Through the smart contract design, the zero-watermark information is registered, and the transaction information is stored permanently in the blockchain to realize the time-stamp authentication of zero-watermark registration. The tamper-resistant of blockchain and the automatic execution mechanism of smart contract in the model enable both parties to reach a consensus and achieve mutual trust without the supervision of a third party. Based on this model, this paper uses "Ant OpenChain" as a smart contract development platform to develop a transaction and copyright protection system for geographic data. With this system, both sides of the transaction can call the smart contract to obtain the transaction certificate from the blockchain. Then, the watermark information is extracted by the zero-watermark downloaded from the IPFS network. Finally, the data copyright can be confirmed and the source can be traced effectively for protecting rights or solving disputes by combining the transaction certificate and watermark information to confirm. Experiments show that the proposed method can protect the rights and interests of both parties and protect the security of transaction data simultaneously.

The selection of road network is a fuzzy decision-making process. Aiming at problems such as noise, conflict and human decision-making in the cartographic generalization algorithm based on general case reasoning, this paper proposes a road network selection method which uses ontology to organize cases and conduct knowledge reasoning. In this paper, we extract expert cases from series scale map and describe them formally according to ternary model. To realize knowledge reasoning, these cases are used to build a domain ontology repository, reorganizing their representation format. Then, ontology rule extension helps to identify and eliminate noise and conflict cases. Additionally, with methods of semantic mapping and graph query, the semantic and geometric features of the road network to be selected are associated with the ontology repository to realize road network selection automatically.

Whether the location is accurately described or not directly affects the quality of service of LBS. Relative location description is a common way of location information exchange within the text big data, and its core element lies in the selection of reference landmarks. Accurate and effective evaluation of landmark influence is an important foundation for correct selection of a reference landmark. The models involved in the current research focus on the impact on the inherent characteristics of landmarks. However, non-isolated landmarks are always subject to the dual constraints on their internal characteristics and external landmarks at the same time. Therefore, the influence of distance between landmarks can not be ignored in actual scenes. This paper evaluates the individual influence of landmarks from the aspects of cognition, vision and scene, and calculates the relative influence of each landmark on the point to be located by using the distance between the point and the landmark. A landmark influence evaluation model considering distance constraints is proposed to solve above problems. Finally, take multiple adjacent landmarks in the surrounding area of Bayi square in Nanchang as an example, an on-site questionnaire surveys was performed to verify the effectiveness of the evaluation model. The results show that the model proposed in the paper has a good performance and most recommended landmarks could meet people's psychological expectations than the existing models.

Extracting accurate road curb is a crucial task for driverless vehicles. However, existing road curb points extraction methods are not robust for sparse 16-ray LiDAR data. This paper presents a road curb points extraction algorithm that combines multiscale spatial features and measuring distance. The points outside the road areas are firstly removed by adopting the random sample consensus (RANSAC) algorithm, then most of the road surface points are removed by judging the horizontal and vertical continuity between points in the same laser beam. According to the measurement model of the road curb points, if the measured distance of the reserved points is within a reasonable distance and the angle between the two horizontal vectors starting with the point is larger than a certain threshold, the point will be identified as the road curb point. Experiments show that the road curb points extraction method proposed in this paper performs better than the other methods under 16-ray LiDAR dataset and meets the real-time requirements for environmental perception of driverless vehicles.

Road intersections are important components of a road network, which are not only numerous and diverse in shape, but also complex in structure and different in size. It is difficult to recognize comprehensive and accurate road junctions based on single data source, as its limited describe information. To this end, this paper designs a multiple integration method to identify road intersections from vehicle trajectories and remote sensing images. Firstly, based on the unsupervised idea, a method combining morphological processing, density peak clustering and tensor voting is proposed to extract the seed intersections, which is regarded as a small sample set. Based on it two intersection classifiers based on deep convolution network and oriented to vehicle trajectories and remote sensing images are constructed by using collaborative training mechanism, and finally, the advantages of the two models are combined to form an integrated classification model of road intersections. In this paper, a semi supervised intersection extraction technology is proposed by fusing the complementary description features of vehicle trajectories and remote sensing images on multiple levels, which can effectively identify complex and diverse road intersections without manual labeling. Experiments based on Wuhan taxi trajectories and remote sensing images show that the accuracy of this method is more than 93% and the recall rate is 87% without manually labeled samples.

Visual saliency modeling of driving scenes is an important research direction in intelligent driving, especially in the areas of assisted driving and automatic driving. The existing visual saliency modeling methods for static and virtual scenes cannot adapt to the real-time, dynamic and task-driven characteristics of road scenes in real driving environments. Building a visual saliency model of dynamic road scenes in real driving environments is a challenge for current research. Starting from the characteristics of driving environment and driver's visual cognitive law, this paper extracts low-level visual features, high-level visual features and dynamic visual features of road scenes, and combines two influencing factors of speed and road curvature to build a visual saliency calculation model of driving scenes based on logistic regression model (LR). In this paper, the AUC value is used to evaluate the model, and the results showed an accuracy of 90.43%, which is significant advantage over traditional algorithms.

From the automatic driving vehicle to the intelligent vehicle, and then to intelligent connected vehicle, the intelligent driving vehicle technology and industry has developed rapidly, surveying and remote sensing technology have played an important supporting role. Firstly, this paper introduces the development of intelligent vehicles at home and abroad and their differences from conventional vehicles. Based on the perspective of three important waves of scientific and technological development, this paper comparatively analyzes and summarizes the development process and core technology driving forces of autonomous driving and surveying remote sensing. The intelligent driving vehicle's key technologies are then introduced from four aspects: top-level planning and policy environment, environment perception and computing decision-making vehicle key technology, high definition map and navigation positioning basic support key technology, and vehicle road collaborative information interaction key technology. Several advanced navigation and positioning technology achievements, such as satellite-based navigation enhancement, high precision position and attitude measurement, multi-source fusion perception, are taken as examples to demonstrate that surveying and mapping can enable intelligent driving of a single car. By introducing mobile measurement and crowdsourcing high precision map-making technology, the key supporting role of surveying and remote sensing for intelligent driving is expounded. This paper takes the construction of space-based information real-time service system as an example to describe the development trend of how surveying, mapping, and remote sensing will serve the "person vehicle road cloud" of intelligent connected vehicles in the future.Finally, the paper summarizes and analyzes the problems that need to be solved in developing intelligent (connected) vehicles and the challenges that need to be addressed in the near future.

Accurate monitoring of surface water is an important basic application of remote sensing. The principle of optical remote sensing water extraction is based on different ground features having different spectral reflection characteristics. Some ground features (ice, snow, shadows, clouds) have similar reflection characteristics to water bodies, which leads to the failure of extraction and classification. Aiming at the problem of misclassification and omission of traditional water body index in water body extraction, this paper proposes a normalized difference multi-band water index model. This paper uses two experiments to test the stability of the new index. The area of experiment 1 is the Linzhi area of Tibet. The data source is Landsat 8 and Sentinel 2 satellite images in the same time phase. The experimental results verify the ability of the new index to suppress snow and ice. The Kappa coefficient of the new index is 0.86, the overall accuracy is 0.93, and the misclassification error is 0.03, the omission error is 0.12, the drawing accuracy is 0.97, and the producer accuracy is 0.88, which are better than the existing index. The data source of experiment 2 was GF-1, and Hong Kong Disneyland was used as the experimental area. Experiment 3 extracted water bodies in multiple regions and verified the stability of the water body index in this paper. Water extraction was performed in an environment with a small amount of clouds, which proved that the new index can suppress clouds and their shadows. This paper uses multi-source optical remote sensing image to verify the feasibility of the new index. Without additional auxiliary data, the influence of snow, cloud and shadow can be eliminated, and the water can be more effectively and automatically extracted, which can be extended to coastal resource research, glacier change, inland lake change and other fields.

Aerosol optical depth (AOD) is a basic parameter of total aerosol content, which plays an important role in the study of atmospheric air quality. In order to explore the impact of different types of AOD on air quality, this paper proposes two adaptive AOD prediction methods based on GNSS PWV. The proposed method considers the temporal autocorrelation of AOD between adjacent epochs, and the model coefficients can be updated adaptively. One method is to model the 550 nm AOD directly based on GNSS PWV, which is called TAF (total AOD forecast) model. Another AOD modeling method takes into account the sensitivity of five different types of AOD to PWV, referred to it simply as FTAF(five type based AOD forecast) model. The model first establishes the functional relationship between PWV and five types of AOD. Secondly, according to the relationship between 550 nm AOD and five types of AOD, the weight of different types of AOD in 550 nm AOD was determined. Finally, PWV is used to predict five types of AOD, and the final 550 nm AOD is obtained by weighted average. 16 GNSS stations in Beijing, Tianjin, Hebei region are selected to verify the accuracy of the proposed model. The results show that the two 550 nm AOD prediction models have high accuracy, and the FTAF model is better than the TAF model. The AOD prediction model proposed in this paper can effectively apply the tropospheric parameters retrieved from GNSS to remote sensing monitoring of atmospheric environment, which provides a new idea for the study of atmospheric environmental quality.

With the development of Global Navigation Satellite Systems (GNSS), the multipath effect that was once regarded as one error source has been proven can be used to monitor reflecting surface parameters such as water level, snow depth, vegetation water content, and soil moisture using a technology named GNSS-interferometry reflectomety (GNSS-IR). In order to expand monitoring objects and applications of GNSS-IR, this paper proposed the theory and method to retrieve wind speed using ground-based GNSS-IR. The theory includes the signal-to-noise ratio (SNR) multipath oscillation theory, the scattering model theory, and wave spectrum theory. This paper simulated the mathematical model between the SNR cutoff elevation angle and the wind speed, and used the wavelet analysis to extract SNR cutoff elevation angle and a unified method to unify reference of cutoff elevation angle. Data of HKQT station, located in Hong Kong, during the period of "Mangkhut" and "Hato" were used. The results showed that the variations of GPS L5 SNR cutoff elevation angles had a good corresponding relationship with the measured wind speeds, and the correlation coefficient reached 70%~85%. Also, the variations of cutoff elevation angles can describe the change of wind speed from low wind speed to high wind speed and there was a specific mathematical relationship between them. Finally, this paper discussed two difficulties to overcome in the GNSS-IR wind speed retrieval technology.

Urban traffic efficiency is one of the key factors affecting urban productivity and is also a crucial topic in the process of smart city construction. With the development of computer technology, artificial intelligence, especially reinforcement learning, plays an increasingly important role in traffic signal control. Currently, traffic signal control based on reinforcement learning is mainly used for the optimization for simple scenarios, such as single road intersection or urban arterial road, not yet for regional coordinated control on an urban geographic road network. This paper is motivated to fill this gap by proposing a two-layered agent cooperative control approach based on reinforcement learning. The first layer implements a coarse-tuning training at a single intersection, where the agents make the single intersection non-blocking by observing the queue length for each lane; In the second layer, the coarse-tuning-trained agent models are put into the geographic network to execute the cooperative fine-tuning training at multiple intersections. This paper conducts the optimization-orientated traffic coordination through a case study of a middle school area in Ningbo. The results show that our control approach is superior to the traditional fixed timing scheme in terms of the passage efficiency.

The ranging accuracy of spaceborne laser altimeter is one of the main sources that affect the geometric calibration and processing accuracy of the laser.Aiming at the issues of laser ranging extraction lower accuracy and the stability, caused by the quantization error of the full-waveform spaceborne laser analog signal after digital processing, so a sliding window Gaussian fitting algorithm for ranging error suppression of full-waveform spaceborne laser is proposed.This method uses a sliding window to eliminate noise points near the peak, and optimizes the peak value of the waveform to extract a more accurate ranging value.Finally, the GF-7 laser altimeter was used as the test object, the relative and absolute accuracy of laser elevation were compared and verified by using the ice surface, inland lake and the flat ground.The results indicate that, the laser ranging accuracy of the paper algorithm is improved by 7.5 cm compared with the general peak method. Based on the ranging value of the paper method, the relative accuracy of laser elevation is improved by 4.3 cm. The absolute accuracy of laser elevation is increased by 4.5 cm verified by airborne LiDAR point cloud data. Which fully shows that the method in this paper can be used as a method to reduce effectively the random error of laser ranging, and provides an indispensable foundation for the sub-meter elevation measurement accuracy of the GF-7 satellite.

The dynamic precision engineering survey is not only used in the construction but also in monitoring the dynamic change during usage. With the rapid popularization of various sensors and automated mobile platform, such as intelligent vehicles, unmanned aerial vehicle (UAV), unmanned ship, robots etc., the engineering survey is upgraded in an automated, dynamic and intelligent way. It is now armed with the ability to survey in movement. Surveying devices, including mature surveying robots, mobile surveying vehicle, etc., have been proposed. Another way is to assemble devices delicately for specific applications, which can achieve dynamic and precise measurements of position, posture, internal deform and external shape. Focusing on the intelligent device with integrated sensors and specific applications, this paper puts forward the integration principle of multiple sensors and the advanced data processing methods for intelligent engineering surveying. This paper also demonstrates several applications of these devices, such as internal deformation monitoring of the rockfill dam, damage monitoring of the urban underground water pipelines, surveying of the road damages and the damage detection on the south-to-north water diversion project (SNWDP), to show different requirements and solutions in different applications.

This paper proposes the concept of surveying robot, focuses on analyzing the framework and key technologies of the surveying robot, covers the cutting-edge research fields of surveying robots such as positioning and mapping, scene perception, path planning, and multi-machine collaboration. This paper also summarizes the latest research progress, analyzes the current technical problems and describes the development opportunities of surveying robot.

With the rapid development of geospatial science, artificial intelligence, and high-performance computing, geospatial artificial intelligence has become the major technique for processing and analyzing geospatial big data, and will be widely used in the scientific research and engineering application of earth science, spatial cognition, and smart city. Geospatial intelligence, as an inter-discipline between geospatial science and artificial intelligence, is driven by dual disciplines. At present, it has made important progress in hardware research, system development, data and model sharing, services and applications, and is also facing new challenges. This paper first describes the conceptual evolution of geospatial artificial intelligence and lists the frameworks of some technical systems. The paper then reviews the state-of-the-art in the scientific research and typical application domains. The problems and challenges facing geospatial intelligence are analyzed. Finally, some future prospects and trends are presented.

Geographic knowledge service (GKS) is widely believed to be the intelligent successor to the current geographic information service (GIS). The need for GKS is becoming ever pressing due to the increasing severity of the information explosion problem. This paper clarifies the confusions in the connotation of GKS, its relationship with GIS, and its requirements for formal representation and intelligent processing of geographic knowledge. After an in-depth analysis of the latest breakthroughs made in artificial intelligence (AI), it is argued that the state-of-the-art AI provides a promising basis of cognitive intelligence for advancing GKS development. Emphasizing the spatio-temporal characteristic of geographic knowledge, this paper identifies three main categories of research issues in GKS development, i.e. those of the knowledge engineering approach to geographic modeling, the cognitively intelligent approach to geographic analysis and the context-aware computing approach to service provision. A graded strategy for advancing GKS is then suggested and a roadmap of GKS development is envisioned.

Carrying out an integrated survey, monitoring and evaluation of natural resources, accurately understanding the status and changes of various natural resources in China, is the scientific basis for territorial and spatial plans, and gradually realizing the overall protection, restoration, and comprehensive management of landscapes (including mountains, forests, fields, lakes and grasses), ensuring national ecological security. At present, the extraction of natural resource features based on remote sensing images mainly relies on visual interpretation via man-machine interaction and field spot verification. It needs high labor intensity, and production efficiency is low. The results are also highly affected by man-induced factor, which can no longer adapt to the requirements for integrated investigation and monitoring of all features of natural resources. This paper conforms to the emerging direction of the research development with artificial intelligence collaboration. Firstly, this paper reviews the main research progress of deep learning technology and its application in the field of remote sensing image intelligent extraction systematically, and analyzes its limitations, then it reviews the main research status of man-machine collaboration. Afterward, Starting from the characteristics of natural resource features, presents a technical framework of "intelligent background computing+intelligent engine+man-machine interface" for man-machine collaborative intelligent extraction. The key technologies that need to be overcome are described. At last, the idea of creating cloud platform for feature extraction are discussed. This paper aims to provide a new AI method for intelligent extraction and improve the automation and intelligence level of natural resource feature extraction.

In this paper, we reviewed the operational topographic surveying and mapping capability, i. e., plane and height accuracy, that synthetic aperture radar (SAR) satellites achieve. The plane accuracy of TerraSAR-X, COSMO-SkyMed and Tianhui-2 meets the need of 1:50 000 digital orthophoto map standard. The state-of-the-art plane accuracy is 2 m which is achieved by TerraSAR-X. Meanwhile, the accuracy of operational ground control point production provided by TerraSAR-X is 3 cm. We reviewed the error sources and the calibration methods for centimetric SAR image location. The height accuracy of TanDEM-X and Tianhui-2 meets the needs of 1:50 000 digital elevation model standard. The state-of-the-art height accuracy is 0.88 m which is achieved by TanDEM-X if the dense vegetation areas and the snow-and-ice covered areas are exclusively included in the statistical results. We reviewed the error sources and the calibration methods for height measurement without ground control information. The paper is expected to provide references for the geolocation and height measurement of the domestic SAR satellites.

GNSS three-dimensional water vapor tomography technology is used to obtain all-weather atmospheric water vapor distribution information with high spatial and temporal resolution. At present, the observation data of the tomographic model considering only the top signal have low utilization rate and large grid space rate, and the vertical constraint equation adopted is less consistent with the actual water vapor distribution. This paper based on the function fitted with sounding information to establish a vertical constraints, designs and implements a tomographic method considering boundary signals, uses the observation data of Hong Kong CORS network and radio sounding products. The improvement of the results of water vapor tomography by introducing boundary signals and for vertical constraint equation are analyzed in detail. The results show that the addition of boundary signals increases the observation equation by 51.9%, decreases the grid space rate significantly, and reduces the root-mean-square error of the solution results by 12.1%. Compared with the traditional constraint equation based on the exponential decline characteristic in the vertical direction, the root-mean-square error of the vertical constraint solution adopted in this paper is reduced by 5.7%.

The Bootstrap resampling method is introduced to the nonlinear theory for solving the precision estimation in this paper. By resampling the original sample observation data or the residuals of the dependent variable to obtain Bootstrap samples instead of the complex derivative calculations, the complete algorithms of Bootstrap method for solving the problem of nonlinear accuracy evaluation are given. Aiming at the equal probability resampling of model stochastic variable, this paper obtains the empirical distribution function of the stochastic variable in the sampling process, proposes the weighted sampling strategy, and gives the detailed calculation steps of the improved method for the accuracy evaluation. The results of experiments show that the Bootstrap method based on the resampling observations and the Bootstrap method based on the resampling residuals have stronger applicability, and can obtain more reasonable parameter standard deviations than the approximate function method and Jackknife method. Furthermore, the weighted resampling Bootstrap method based on the resampling observations and the weighted resampling Bootstrap method based on the resampling residuals can obtain more accurate precision information with extensive advantages. Those which verified the feasibility and effectiveness of using Bootstrap method and the improved algorithms proposed in this paper for precision estimation of nonlinear adjustment.

There are significant nonlinear intensity differences between multi-modal images. Moreover, the noise in these images will cause image degradation. Therefore, the automatic registration of multi-modal images is a challenging task. To address the two problems, this paper proposes a multi-modal image automatic registration method, which is divided into two stages: pre-registration and fine registration. In the pre-registration stage, an improved SIFT algorithm is used to roughly align multi-modal images. In the fine registration stage, the block Harris detector is first used to extract evenly distributed feature points on the pre-registered reference image. Then, the structure information in the multi-modal images is captured by the anisotropic structure tensor to construct a feature descriptor, which is robust to noise. Furthermore, a similarity criterion named TOMI (tensor orientation and mutual information) is proposed combining the tensor orientation parallelism and gradient mutual information. Finally, Multi-modal images (including Optical, LiDAR, SAR, and Map data) are used to evaluate the proposed algorithm. The experimental results show that the method proposed in this paper is robust to nonlinear intensity differences and noise, and the matching effect is superior.

Provenance is an important research issue of “Neogeography”, and it plays a significant role in judging whether geospatial data is reliable or not. In a distributed collaborative environment, geospatial data provenance also faces reliability issues. Blockchain technology has gained rapid development in recent years due to its characteristics of credibility, transparency, and non-tampering. It provides a new solution to the reliability management of provenance records. However, how to use the blockchain to organize and store a large number of geospatial data provenance with different granularity levels remains a challenge. Therefore, taking vector data as the research object, this paper discusses the requirements in designing the structure of provenance blockchain, and proposes a method of organizing on-chain provenance information at different levels based on Merkle Patricia tree (MPT). Further, an algorithm that is suitable for the verification of vector provenance information is provided.This paper develops a vector data provenance blockchain prototype system, and conducts verification experiments using the vector provenance information. The experiments demonstrate that MPT can achieve higher performance than binary Meckel tree in vector data provenance verification.

Spatial cognition affects human spatial behaviors. The study of cognitive process is of great significance in map and navigation design, geography teaching and so on. Traditional cognitive experiments mostly use maps (including paper maps and electronic maps, street view maps, etc.) and real scenes as experimental environments to study the psychological process of subjects’ reading maps and the guidance effect of maps in real geographic environment. This paper puts forward the concept of earth view, that is, taking the third person reading scene of Google Earth as the experimental environment, studying how the candidates orientate and locate the places of countries or regions and search for the target on the Earth without fixed axis through certain operations on Google Earth, and analyzing the cognitive process of the whole earth sphere. By visualizing the process of reading the Earth with the gaze point data recorded by eye tracker and analyzing the process of thinking, it is found that on the earth, the target searching process, which combines relative position judgment and orientation positioning, is the comprehensive presentation of construction process about the candidates’ Earth view.

At present, eye tracking technology has been widely used in human-computer interaction, user behavior recognition and prediction, but how to automatically identify user’s eye movement behavior in map reading is still a challenge. This paper proposed a method based on the naive Bayesian classification model to identify the users’ behavior when performing linear feature recognition. We first conducted an eye tracking experiment to acquire users’ eye movement dataset of map reading. Then we extracted and discretized 250 eye movement features involved in the algorithm, and used minimum redundancy maximum relevance algorithm to further select the features. The results show that when the attribute selection method is m=5 using mutual information quotient, the classification accuracy is 78.27%. And when using mutual information difference and m=4, the classification accuracy is 77.01%.We suggested that the proposed method can effectively identify the first elements in the map using eye movement data. This study explores the interaction technology by combining the eye tracking, laying the foundation for the future of designing gaze-controlled interactive map. The proposed method based on naive Bayesian model in this paper is comparable to the existing methods. In addition, the execution efficiency of the model is greatly improved due to the reduction in the number of features. The eye-track recognition algorithm of map reading behavior proposed in this study lays a foundation for future gaze-controlled interactive map research.

Focused on the geographic spatial cognition, this paper introduces the origination, the current situations, the four classical geographic spatial cognition theories and the difference between two factions. In the following, for the important role of map in spatial cognition, the function and research methods are concluded. Especially, the eye-tracking technology are paid more attention to, for its advantages in evaluation and interaction. Eventually, three developing directions and the characters of geographic spatial cognition in new AI era are discussed. With a focus on theoretical issues of geospatial cognition, this paper addresses at first their origin in psychology and the state of the art. The classical theories of geospatial cognition and their impacts are then introduced. Meanwhile, the fundamental difference between the geospatial cognition and geospatial sensing is pointed out. Although both are supported by the Artificial Intelligence (AI), the former is targeted to the understanding of how human brain works for spatial tasks, whereas the latter aims to maintain a dynamic digital twin of the geospace based on sensory technology and computer vision. The role and research methods of maps as a two-way tool for spatial cognition are discussed, with emphasis on eye-tracking experiments as well as their advantage in interactions. Three development trends of geospatial cognition in the era of AI—computational semantic cognition, cognitive mechanism of the mind and embodied cognition are summarized. The parallel development and characteristics of map-based spatial cognition in this same period are outlined.

Aiming at the ill-posed problem of multiplicative error model, this paper introduces the Tikhonov regularization method to derive the weighted least squares regularization solution. Considering the complex nonlinear relationship between parameter estimations and the observations when using weighted least squares regularization method to solve the ill-posed multiplicative error model, the scaled unscented transformation (SUT) method is used to calculate the mean value and mean square error matrix of the nonlinear function by weighted without derivation for precision estimation of ill-posed multiplicative error model. The simulated and actual examples results show that the weighted least squares regularization iterative solution proposed in this paper can effectively weaken the ill-posed model, and the precision estimation method based on SUT method can obtain more reasonable precision information than the existing methods, and has strong applicability.

Multi-temporal interferometry synthetic aperture radar (MT-InSAR) has the capability to monitor deformation with millimeter precision over wide areas. It has been widely applied for the monitoring of land subsidence. In recent years, much attention was paid on monitoring of the subsidence along large-scale man-made linear features represented by high speed railway(HSR) with MT-InSAR. This paper conducts a tentative test for applying C-band SAR data to the deformation monitoring of HSR subgrade, phase stability analysis and improved StaMPS technology were jointly used to increase the point density and stability of deformation parameter solution. In this paper, a total of 47 Sentinel-1A images spanning 21 months were processed for MT-InSAR analysis along Lian-Yan HSR, and the accuracy of linear deformation velocity and time-series deformation were evaluated respectively by the data of continuous BDS (BeiDou navigation satellite system) monitoring station. The results demonstrate that C-band Sentinel-1A data is capable of achieving millimeter accuracy in linear deformation velocity and time-series deformation, the mean root mean square error (RMSE) value of time-series displacement differences between the InSAR and the BDS is 3.8 mm, agreed well with BDS monitoring data, and the overall performance of Lian-Yan HSR is stable.

In order to solve the problem that the traditional algorithm for multibeam sounding coordinates ignores the significant impact of the difference in the position of the receiving and transmitting transducer on the deep-water sounding, and the new algorithm takes into account the difference in receiving and transmitting but low efficiency, this paper proposes a high-precision and quick algorithm for multibeam sounding coordinates that considers the propagation surface. First, based on the principle of multibeam measurement and sound ray propagation, a beam propagation surface model is proposed. Second, the beams in the sector are divided into interpolation nodes and points to be interpolated. For the former, a method based on iterative search and interpolation is proposed to avoid the establishment of a complete propagation surface. For the latter, using the interpolated node parameters, a method based on polynomial interpolation is proposed to obtain the single-way time, depression angle and azimuth angle of sound ray. Finally, all beam footprints are calculated. Experiments in shallow and medium-deep water areas show that the accuracy of the cross-line inspection method using this paper method is similar to that of Caris, and is better than the traditional algorithm. Compared with the results of Caris, the depth deviations of the same beam footprint in this paper are 0.35‰ depth and 0.11‰ depth, respectively, and the efficiency in this paper has increased by 8% and 35%, respectively.

The weighted mean temperature Tm is a key parameter of the global navigation satellite system (GNSS) inversion of precipitation. Taking the Shaanxi area in China as an example, this paper combines the reanalysis data of the European Weather Forecast Center (ECMWF) with the data of three sounding stations, and establishes a Tm regionalized regression model considering periodicity based on the principle of least squares. The data from three radiosonde stations in Shaanxi Province were used for verification. The results show that the Tm regional model established in this paper taking into account the cycle has an average improvement rate of 16.1% compared with the traditional Bevis model. In addition, in view of the differences in regions with different climate types, this paper establishes a sub-climatic zone Tm model with a piecewise linear form that changes with latitude, and solves the problem of adaptability of the regression model in different climate zones. Compared with sounding data, the Tm model that takes into account the climate difference has an external accuracy (RMS) range of 1.47~2.06 K. Compared with the Bevis model, the average accuracy improvement rate is 44.9%, and the improvement effect is significant; using ECMWF data to select 19 each grid point evaluates the accuracy of the model. The results show that the average RMS is 3.26 K and the maximum RMS is 3.67 K; the average STD is 2.69 K and the maximum STD is 3.19 K.

The ionospheric scintillation, as one of the astronomical disasters occurring frequently in Arctic regions, poses great challenges to GNSS positioning navigation and timing (PNT) services. This calls for an urgent need in studying and effectively monitoring the scintillation to overcome its adverse impact. With the capability of high frequency sampling, ionospheric scintillation monitoring receivers (ISMR) are usually required to monitor the ionospheric scintillation, but the distribution of ISMR restricts the comprehensive monitoring in larger areas (such as the Arctic region). Therefore, based on GNSS observations with 1 Hz sampling, this paper studies the relevant empirical parameters and methods of extracting the ionospheric scintillation signal from the carrier phase observations by using geodetic detrending, precise point positioning and wavelet transform techniques, to construct a new phase scintillation index, which can be used to monitor the ionospheric scintillation. Its effectiveness and accuracy are verified by 188-day observations from 11 stations provided by the Canadian High Arctic Ionospheric Network (CHAIN). The results show that, compared with the commonly used ROTI index, both the scintillation index proposed in this paper and ROTI can effectively detect the occurrence of ionospheric scintillation, but the scintillation index proposed in this paper has a better correlation with the phase scintillation index given by ISMR, especially during periods with strong ionospheric scintillation, indicating that the proposed scintillation index has better ionospheric scintillation monitoring capability.

For high-speed and high-sampling-rate continuous real-time dynamic (RTK) positioning, fast cycle slip detection plays an important role. In this paper, based on the noise characteristics of ultra-high sampling rate data and the modified geometry-free (MGF) combination, a low-latency cycle slip determination method is proposed for kinematic GNSS data, named OMGF. Based on the noise characteristics of high sampling rate observation, cycle slips are preliminarily repaired by the pseudo-phase combination, and then MGF is used to perform secondary repair on the initial repair results. This paper deduces the success rate of OMGF cycle slip repair for pseudorange and carrier phase observations with different noise levels for different satellite systems, and the observation noise boundary conditions for OMGF method. OMGF method only needs the observation of a single satellite to detect and repair cycle slip, and extend the accuracy requirement of pseudorange observation for cycle slip determination. The experiment results with 20 Hz GPS/BDS/GLONASS measurements show that, the success rate of OMGF method is about 99.998%. Compared with the combination of GF and HMW, OMGF method is about 60 times faster. OMGF can perform cycle slip determination at low complexity and high calculation efficiency, which is beneficial for the mobile terminal with limited computing power resources to quickly process ultra-high sampling rate GNSS data online.

Instruments, surrounding environment and human operation often cause a lot of noise in the LiDAR, resulting in low model regression accuracy. RANSAC algorithm is widely used to solve model regression problems by virtue of its simple implementation and robustness. However, for different scenarios, RANSAC algorithm needs to constantly adjust the parameters to estimate the optimal model solution. Considering the RANSAC algorithm and its family existing shortcomings, according to the difference of density distribution between inliers and noise. This paper firstly optimizes the initial hypothesis model by using density weighted guided sampling, and then proposes a spatial density function to evaluate the optimal model and to calculate the number of iterations by using the spatial density function. The whole process does not need any prior knowledge. The method proposed in this paper can solve the model regression problem where the inliers ratio is more than 10%. In addition, compared with the existing methods, the method proposed in this paper can achieve high accuracy and robustness without prior information.

The spaceborne atomic clock is the core equipment of Beidou Satellite Navigation System (BDS). Clock error model is an important part of BDS broadcast messages, and its accuracy directly affects the service performance of BDS. In this paper, the accuracy of the BDS clock model is evaluated using the TWSTF clock error measurement as a reference. The results show that the accuracy of the BDS clock model is better than 2 ns (data age less than 12 h). Aiming at the problem of calculating the clock error parameters of BDS, combined with the clock error model solution strategy of the BDS ground segment, this paper proposes a variety of strategies to further improve the accuracy. For the short-term forecast of 1 h duration, this paper proposes a clock error forecast method of weighted linear model and mixed interval linear model. The accuracy of short-term forecast can be increased by more than 20%. For medium and long-term (>6 h), clock error time series is analyzed by spectral method and prediction model is constructed according to the spectral analysis results. Compared with simple quadratic polynomial model, the prediction accuracy of the clock error model is improved by 13% in 6 h and 21% in 12 h on average. For IGSO/MEO satellite, this paper proposes a strategy of using TWSTF equipment delay as parameters to estimate, which improves the accuracy of clock error model by 18% after equipment switching. Finally, using the improvement strategy proposed in this paper, the clock error data from January to June 2017 is reprocessed to obtain a new broadcast clock error model time series. Using the new broadcast clock error model to locate the BDS monitoring receiver, the results show that, the direction accuracy of N, E and U is increased by 14.22%, 29.39% and 14.91% respectively, which significantly improves the service performance of BDS broadcast clock error model.

Cropland is a scarce land resource in hilly and mountainous areas, which has the characteristics of complex topographic conditions and diverse planting structures, leading to the difficulty of rapid and accurate acquisition of cropland information in mountainous areas. Therefore, it is difficult to extract the cropland information in mountainous areas quickly and automatically based on the traditional remote sensing data and remote sensing monitoring methods. Aiming at this problem, this paper takes Xifeng County of Guizhou Province in the southwest mountainous area as the experimental area. According to the heterogeneity of geospatial space, this paper proposes the idea of cropland morphological information extraction by geographical division control and stratification extraction, and constructs a method for extracting cropland morphological information based on geographical division control and stratification extraction under the constraints of geomorphic unit. Firstly, according to the geomorphology-vegetation characteristics, the experimental area is divided into three geographical zones: flatland, hillside area and forest. Then, on the basis of each type of partition, the cropland is divided into different types according to the visual characteristics presented by the cropland, and different deep learning models are designed for hierarchical extraction of different types of cropland. The experimental results show that this method has a good suppression effect on the background noise of complex terrain in mountainous areas, and the extracted cropland plot information is more consistent with the actual distribution pattern of the actual cropland compared with the traditional method, which effectively reduces the rate of missing extraction and wrong extraction.

The spatial relationship in the paper means the spatial adjacency relation between the objects in word space, and the paper proposes a novel image matching method based on analyzing the spatial adjacency relation between the object contained in the image pair. In the method, the feature extraction network is first trained based on comparison mechanism, and the well-trained model could produce the deep features for the objects, which could effectively match the consistent objects across images. Then the priori images are employed to deduce the spatial adjacency information between different objects, which is further represented by the spatial adjacency graph. Finally, the spatial relation matching between the image pair is conducted by calculating the spatial adjacency score based on the spatial adjacency graph. The experimental results demonstrate that the spatial adjacency scores of the non-matched image pairs generally equal to 0, and those of the matched pairs are generally greater than 0. As several objects are involved, the proposed spatial relation matching method could achieve high robustness, it outperforms other methods in the comparison experiments, and it could effectively complete the image matching task with high efficiency.

Seafloor classification is of great significance for the development and utilization of marine resources and marine scientific research. At present, multibeam detection is one of the effective methods to achieve large-scale seafloor classification. Seafloor classification is usually based on the angular response (AR) features and backscatter image features extracted by using multibeam backscatter. Because the feature source is relatively single and classifier structure is simple, the classification accuracy is often not high. This paper proposes a seafloor classification method based on convolutional neural networks (CNN). In addition to backscatter features, bathymetry features are also used to classify. The feature vectors are converted into waveform maps, and then input to the convolutional neural network for training and classification. The experiment compares different feature combination models and four conventional classifiers: BP network, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The overall classification accuracy of the experiment reaches 94.86%, the kappa coefficient up to 0.93, and it takes 1 min 25 s. The accuracy has obvious advantages and the efficiency is relatively high. This method can effectively obtain the seafloor information in two different data types, give full play to the characteristics of convolutional neural network weight sharing, high efficiency, and achieve high-resolution seafloor classification. This paper provides a reference for the seafloor classification based on multibeam.

Using deep convolutional neural network (CNN) to intelligently extract buildings from remote sensing images is of great significance for digital city construction, disaster detection and land management. The color difference between multi-temporal remote sensing images will lead to the decrease of generalization ability of building semantic segmentation model. In view of this, this paper proposes the attention-guided color consistency adversarial network (ACGAN). The algorithm takes the reference color style images and the images to be corrected in the same area and different phases as the training set and adopts the consistency adversarial network with the U-shaped attention mechanism to train the color consistency model. In the prediction stage, this model converts the hue of the images to that of the reference color style image, which is based on the reasoning ability of the deep learning model, instead of the corresponding reference color style image. This model transforms the hue of the images to be corrected into that of the reference color style images. This stage is based on the reasoning ability of the deep learning model, and the corresponding reference color style image is no longer needed. In order to verify the effectiveness of the algorithm, firstly, we compare the algorithm of this paper with the traditional image processing algorithm and other consistency adversarial network. The results show that the images after ACGAN color consistency processing are more similar to that of the reference color style images. Secondly, we carried out the building semantic segmentation experiment on the images processed by the above different color consistency algorithms, which proved that the method in this paper is more conducive to the impro-vement of the generalization ability of multi-temporal remote sensing image semantic segmentation model.

The increasing demands and diverse missions in earth observation lead to the complexity of satellite area coverage analysis, and satellite area coverage algorithms or applications cannot provide real-time response and online services that can handle high concurrent requests. Current analysis results cannot be visualized in real time, which increases the difficulty of data cognitive. To solve these problems, this paper proposes an efficient real-time analysis method and visualization service for satellite area coverage, which is combined with flow processing technology. First, a multi-mode judgment strategy is adopted to calculate the satellite area coverage parameters efficiently. Then, the continuous computing tasks are segmented to implement real-time data processing. Finally, based on streaming processing technology, this paper proposes an online service for real-time calculation and visualization. By using the data which contains three satellites and two ground areas to verify the algorithm, the experimental result shows that the method in this paper can not only ensure the accuracy under different sensor orientations, but can also greatly improve the computational efficiency. It can also provide real-time visualization of the analysis results in a timely and efficient manner.

The pattern structure of buildings in the map generalization is a key factor to be considered. When there are spatial graphic conflicts among subgroups of buildings in different patterns, the generalization of these buildings becomes more complex. The generalization of straight linear pattern buildings is dominated by typification on large scale topographic maps. This paper proposes a progressive typification method for building subgroups of straight linear patterns with potential spatial graphic conflicts. The proposed method can progressively deal with the spatial graphic conflict between building subgroups in multiple straight linear patterns, and retain the important straight linear pattern structure of building groups. The position, shape, size and orientation of the typical building can be determined automatically with the restriction of visual graphical constraints expressed by the building. This paper also studies the automatic identification method of straight linear pattern of building subgroups based on the spatial proximity graph of building groups, and analyzes the adjacent relationship and intersection relationship between these straight linear patterns. Finally, the buildings on the 1:5000 map as a test data were generalized into 1:25 000 map, which verifies the availability and effectiveness of the algorithm proposed in this article.

Data storage model is an important part of database model. This paper aims at bottleneck problems in developing relational spatial database and using HBase to manage spatial vector data. Based on the analysis of relational spatial database storage model, the rules fortransforming the relational database storage mode into HBase storage mode are applied to spatial vector data management, a method of transforming spatial vector data relational storage schema into HBase storage schema is proposed, and a HBase storage model is designed for spatial vector data. The model is optimized and improved by using HBase's features such as entity nesting, denormalization and schemalessness.The experimental results show that the query efficiency of the storage model designed in this paper is better than that of the HBase-based spatial vector data storage model in the absence of auxiliary indexes.

Aiming at the lack of high accurate antenna phase correction (PCC) model for the BDS-2 IGSO and MEO satellites, in the BDS-2 and BDS-3 satellites joint precision orbit determination (POD) and precise point positioning (PPP), in this paper, an improved method for estimating PCV and z-offset parameters is used to refine the B1I/B3I ionosphere-free linear combination PCC model for the BDS-2 IGSO and MEO satellites. Compared with the results obtained using the ground-calibrated PCO model published by BeiDou official, the STD of SLR residuals obtained by the POD using the refined PCC model is reduced by 0.6~2.4 cm, and the improvement percentage is about 8.6%~33.3%. The floating PPP solution results, based on the refined BDS-2 satellite PCC model in this paper and the existing BDS-3 PCC model, have been significantly improved by 9.5 mm (37.2%) in the elevation direction.

In this paper, a 1'×1' bathymetry model BAT_WHU2020 in the range of 75°S—70°N is constructed by using the latest version of the global gravity anomaly model derived from the multi-source satellite altimetry data and the shipboard depths. The accuracy of the model is analyzed and evaluated based on ship depths, existing models and multibeam soundings. The standard deviation of the difference between the proposed model and the ship depths in China Sea and its adjacent areas (104°E—160°E, 0°N—50°N) is about 70 m, which is equivalent to the accuracy of SIO V19.1 model, superior to ETOPO1, DTU10, GEBCO_08 model, and about 30% higher than the accuracy of BAT_VGG model published before, which shows that the method in this paper is reliable, and the data processing is accurate and the accuracy is high. The standard deviation of the differences between BAT_WHU2020 model and ship depths is about 50~65 m globally, and the ratio of the difference within ±200 m is greater than 95%. It is showed that the accuracy of BAT_WHU2020 model is equivalent to SIO V19.1, better than ETOPO1, DTU10, GEBCO_08 model, and improved about 27%~36% from the BAT_VGG model. Comparing to SIO V19.1 model, the standard deviation of the model differences is about 90~110 m, about 90% of the grid differences is within 200 m, and about 95% is within 300 m . Finally, the effects of crustal isostasy and high order terms in the Parker's formula on the accuracy of the results, the accuracy of the model compared to multibeam soundings, and the spatial resolution are discussed. It indicates that the spatial resolution of BAT_WHU2020 model is about 10~18 km, and the relative accuracy is about 5%~6% around the Mariana trench and Macquarie ridge.

The construction of the voxel model is an important part of computer ionospheric tomography (CIT) method. It will no doubt affect the accuracy of the CIT method. However, most existing researches focus on how to improve the image’s accuracy via inversion algorithm and few of them are concerned with the grid construction solution. This paper intends to improve accuracy by making some optimization on the voxel model. Some preliminary experiments were first carried out to explore the relationship between the accuracy of the image and its corresponding resolution. Then, based on the findings, a new CIT method, which is to improve the accuracy of the tomographic image of the interesting area at the price of reducing the resolution of the non-interesting area, was proposed. After that, some further experiments were taken to validate the new method. After comparing various accuracy indicators, such as root mean square error (RMSE), absolute mean error (MAE), 68% percentile, 95% percentile and stand deviation, the new method is showed to be better than the one that is not using the method proposed in this paper. According to our experiments, the new method is able to decrease RMSE/MAE of the tomographic image by 15% to 40%.

Aiming at the problem that the three-dimensional coordinate solution precision is influenced by the non-linear error, a method of combining multi-image forward intersection with single hidden layer BP neural network is proposed in this paper. The steps are: ①In order to obtain the initial value of the three dimensional coordinates with higher accuracy, the external parameters of the camera are optimized by constructing the Lagrange equation about the world coordinates under the constraint of known real world coordinates of the sample point. ②The single hidden layer BP neural network is trained by using the calculated 3D coordinate and the real 3D coordinate as input and output parameters, respectively. ③The initial three-dimensional coordinate is corrected by brought into the model. Experiments show that: ①In the environmental field of view of the test device, the proposed method outperforms the space intersection, the sparse bundle adjustment and the other classic neural network methods, the maximum deviation is 0.492 7 mm. ②Compared with other classic neural network methods, the network structure of this paper is 3-6-3, the structure is simple and the calculation efficiency is high.

In order to obtain the accurate SAR orthophoto over wide area, it is necessary to modify the geometric parameters for each of the SAR images. However, it is time consuming and laborious to correct each of the SAR images over wide area, which seriously decreased the efficiency of SAR image analysis. Therefore, a wide-area joint geometric calibration method is proposed in this paper. Firstly, the independent system-level calibration parameter is calculated using several SAR images on the basis of R-D model. Secondly, the influence of atmospheric delay component is removed from the system-level calibration parameters. Then, the optimal system-level calibration parameters are obtained based on the least squares adjustment principle. Finally, the other SAR images are corrected using the optimal system-level calibration parameters.In this paper, 32 scenes of GF-3 SAR images covering the central and eastern part of China are used for joint geometric calibration. The positioning error of the calibrated SAR images is less than 9 m. The experimental results show that the accuracy of geometric calibration for GF-3 SAR images can be improved by the proposed approach, and thus the effectiveness and feasibility of the proposed approach is demonstrated.

Multipath effect is related to the observational environment of stations, and cannot be eliminated or mitigated by differential algorithm. It is a major factor affecting accuracy and reliability in GPS signal processing. This paper proposes a method to mitigate the multipath effect—MHGM (Multi-point Hemispherical Grid Model). The method divides the hemisphere centered on each station into a grid, and estimates the multipath error at the station based on parametrization of the grid points, which can be applied to different kinds of hardware and software system of GPS data processing and observation environment. The experimental results in this paper show that, MHGM multipath error modeling based on data from observations made improvements in the mean RMS of double-differenced observation residuals averaging about 71.3%, an average increase of 26.9% compared with the traditional sidereal filtering method, the positioning results of 1.7 mm in level and 3.0 mm in altitude are obtained for static observation using real-time kinematic relative positioning mode. MHGM proposed in this paper also can used to assess orientation of error sources around the station, giving guidance which may assist in the physical elimination of sources of multipath error.

The definition and connotation of ubiquitous surveying and mapping are given at first. The differences from the traditional surveying and mapping are analyzed and presented. Since the Internet enables convenient interaction of information between people, the most significant progress of ubiquitous mapping in the Internet age is to meet diverse demands about relations between human beings and the society, as well as human beings and the environment, which means that human beings have become the mapping target. At the same time, ubiquitous mapping also focuses on the dynamic perception of environmental changes, the perception and cognition and corresponding services of the relationship between human beings and the environment. This paper indicates that the Internet cannot meet the demands of coordinated perception to physical world or the coordinated management neither the control of remote wide area due to the virtuality of its position and the too low network time standard. Therefore, the Internet and the Internet of Things shall advance towards the ubiquitous network with the capability of perceiving accurate space-time position. This paper discuss that the ubiquitous network is a perceptive network with space-time characteristics of “Any Time and Any Where” with the evolution of the Internet and the Internet of Things, and is evolving towards “Any Thing and Any Service” with the expansion of the intelligent demands. Currently, only the global satellite navigation system including Beidou, namely GNSS technology, is able to provide the ubiquitous network with space-time position perception and management and control capability in wide area and the global scale. Supported by ubiquitous network, ubiquitous mapping is developing with the characteristics of real-time and accurate space-time perception and control, to realize the perception, cognition, and regulation of land, sea and air network integration, indoor and outdoor integration, and the integration of human beings into physical world and virtual network world as well. As a result, the ubiquitous network will also be the key information infrastructure in the intelligent age. To understand the demands of ubiquitous mapping in the intelligent age, this paper combs through the definition of intelligence, wisdom and artificial intelligence from the perspective of natural intelligence and space-time position. The fundamental, key and coordinated role of ubiquitous mapping is discussed based on the four hot research directions of the new-generation artificial intelligence, including the cross-border integration, human-machine coordination, open collective intelligence and autonomous control, and its application to military, engineering, real-time and high-precision mapping as well as the earthquake early warning. At last, this paper sheds light on the vision of future application trend of ubiquitous mapping to new fields in the intelligent age, such as ubiquitous mapping of the virtual space, construction of digital twin virtual human, and “space and time sovereignty” to national security.

Based on multi-structures convolutional neural networks, this paper proposes an urban expansion cellular automata model (MSCNN-CA) considering the multi-scale neighborhood information to explore the problem of traditional cellular automata (CA) models merely accounting for a single pixel's factors while mining the urban development suitability. This paper took the main urban zone of Wuhan and Pudong New District of Shanghai as examples to simulate the urban expansion process of the two study areas from 2005 to 2015. The experimental results show that, compared with two traditional CA models (LR, ANN), the three single-structure CNN-CA models constructed in this paper have different degrees of improvement in Kappa coefficient, FoM coefficient, hit rate (h) and miss rate (m). In particular, the FoM coefficient is increased by 23.3%~29.4% in the main urban zone of Wuhan and 20.3%~28.5% in Pudong New District of Shanghai. In addition, compared with the three single-structure CNN-CA models, the MSCNN-CA model is also improved in various indicators. Such as, the FoM coefficient is increased by 0.8%~4.8% in the main urban zone of Wuhan and 2.8%~7.8% in Pudong New District of Shanghai. The two study areas' simulation results show that, compared with the traditional CA models, the urban expansion cellular automata model based on multi-structures convolutional neural network (MSCNN-CA) can effectively improve the accuracy of urban expansion simulation and more realistically reflect the evolution process of urban expansion. Besides, both the stability and the accuracy of the MSCNN-CA model are improved comparing with the single-structure convolutional neural network CA model.