The traditional ICP algorithm has long matching time and is affected by initial values in LiDAR target point cloud matching, which leads to low positioning accuracy and poor robustness when applied to unmanned vehicle SLAM technology. Proposes an NDT-ICP algorithm that combines the KD-tree algorithm. Firstly, voxel grid filtering is used to preprocess the point cloud data obtained from LiDAR, and the method of plane fitting parameters is used to remove point cloud of ground. Secondly, use NDT algorithm for point cloud coarse matching to shorten the distance between the target point cloud and the point cloud to be matched. Finally, the KD-tree proximity search method is used to improve the speed of corresponding point search, and the precise matching of the ICP algorithm is completed by optimizing the convergence threshold. Through experiments, it has been shown that the improved algorithm proposed in this article has significantly improved speed and accuracy in point cloud matching compared to NDT and ICP algorithms, and has better accuracy and robustness in map construction.

Imaging systems often face challenges in simultaneously considering both spatial and spectral information. However,existing optical image road extraction methods primarily rely on fused images as data sources,with a focus on aspects such as network structures and supervision forms,without thoroughly exploring and analyzing the impact of fusion effects on road extraction. This paper proposes an optical image road extraction technique that incorporates fusion strategies.Firstly,an “encoder-decoder” network is adopted as the fundamental structure,and targeted improvements and designs are made based on factors such as the categories and quantities of input data,providing a training and testing framework for subsequent experimental verification. Secondly,to favor the injection of spatial and spectral information,four representative image fusion methods are selected. These methods are utilized to fuse panchromatic and multispectral images,providing the necessary technical support.Finally,the experimental section employs two publicly available datasets to demonstrate the effectiveness of the fusion strategy in improving quantitative evaluation metrics for road extraction. Furthermore,the fusion strategy demonstrates a positive and facilitating effect on the extraction of road areas within challenging regions.

The 3D models of cities have been widely applied in various fields such as urban construction and social services. In order to rapidly and accurately construct 3D city models to meet the needs of urban detailed planning and management,this article focuses on the main theme of fast,reasonable,and precise construction of true 3D models,with city terrain and urban features as the research objects. It proposes a fast construction solution for city 3D models,starting from terrain to features,and from rough to precise. It realizes the rapid modeling of urban basic terrain,buildings,and other 3D scenes. The proposed modeling approach is specifically implemented using the Skyline platform,forming a complete operating process.

As an emerging remote sensing technology, GNSS-MR utilizes the multipath effect of GNSS signals during propagation to retrieve surface parameters. It has been widely applied in the field of tidal inversion in recent years. However, traditional GNSS-MR techniques, when extracting the SNR residual sequence using low-order polynomials, often employ a coarse extraction method that does not fully consider the characteristics of GNSS interference signals. As a result, when the satellite elevation angle is large, the extracted SNR residual sequence contains non-interference components, leading to false peak phenomena during LSP frequency spectrum analysis and resulting in inversion distortion. In this study, we leverage the advantages of the variational mode decomposition (VMD) algorithm to adaptively decompose the synthesized signal into a series of intrinsic mode functions (IMFs) with specific physical meanings. We propose an improved GNSS-MR tidal inversion method based on VMD. The tidal inversion results from the SC02 station in Washington State over a period of 100 days indicate that among the three tidal inversion methods based on VMD proposed in this paper, the inversion accuracy and stability are highest when the number of IMFs obtained from VMD decomposition is set to 5 (referred to as VMD-5). Its RMSE is 33.74 cm, correlation coefficient is 0.920 5, and the number of anomalous inversion points is 40. Compared with the traditional inversion method, VMD-4 inversion method, and VMD-6 inversion method, the RMSE is reduced by 89.80%, 10.81% and 48.48% respectively. The correlation coefficients are increased by 305.40%, 2.30% and 25.99%, respectively. And the number of anomalous inversion points is reduced by 83.67%, 6.98% and 14.89%, respectively. The proposed VMD-5-based improved GNSS-MR tidal inversion method effectively reduces the number of anomalous inversion points in traditional inversion methods, significantly improves the accuracy and temporal resolution of inversions, and enables long-term, high-precision, high-stability, and high-temporal-resolution tidal inversion even at large satellite elevation angles.

In order to improve the monitoring ability of slope hazards,this paper proposes a slope anomaly monitoring technology based on deep learning and image local feature extraction. By extracting the two-dimensional coordinates of natural features of the slope,this technology constructs the triangular target network. As the slope danger range is defined by the changing area of the triangular network,feature points with the same name are extracted within the change range,while the displacement of those feature points describes the slope change. The first step is to take images before and after the slope occurs,followed by identifying the natural features of the slope with the target detection model YOLOv5. In the semantic segmentation model DeepLabV3+,the extracted natural features are semantically segmented to obtain their binarized areas,and their two-dimensional coordinates are determined by determining the centre of the binarized area. As a next step,the triangular target network will be constructed by combining the two-dimensional coordinate lattices of all natural features,and the slope change range is delineated as the triangular network changes. After analyzing the image,the feature points with the same names within the change range are extracted using the image feature extraction technology,and their displacement distance and direction are used to evaluate the slope change. According to the test results,this technology is effective at monitoring slope changes,and it is a feasible tool for slope monitoring engineers.

In order to quickly and comprehensively grasp the water environment status of Taipu River,this study takes part of Taipu River as the test area,builds mathematical statistical models of three water quality parameters,including suspended matter concentration,turbidity and transparency,based on UAV hyperspectral data and measured water quality data,and then carries out accuracy evaluation. The model with the highest evaluation accuracy is selected to invert and analyze the water quality of Taipu River. The results show that: ①The hyperspectral band with the highest correlation with suspended matter concentration,turbidity and transparency is about 880～900 nm,and the correlation trend of suspended matter concentration and turbidity is consistent with the reflectance of each hyperspectral band,while the correlation trend of absolute value between transparency and reflectance of each hyperspectral band is consistent with the previous two. ②In the inversion model of suspended matter concentration,the ratio index model has the best effect (test set R²=0.91,validation set RMSE=27.04 mg/L,validation set MAPE=47.04%). And in the turbidity inversion model,the ratio index model has the best effect (test set R²=0.92,validation set RMSE=16.50 NTU,validation set MAPE=15.24%). The normalized index model has the best inversion effect among transparency inversion models (test set R²=0.85,validation set RMSE=2.43 cm,validation set MAPE=8.38%). By comparing the inversion effects of the three water quality parameters,we can see that the inversion effect of transparency is the best,followed by turbidity,and finally the concentration of suspended matter. ③The inverted suspended matter concentration of Taipu River is generally low,while the eastern part is high. Turbidity is generally high in the eastern part,followed by the western part,and lowest in the central part. Transparency is generally high in the western and central part,and lowest in the eastern part.

UAV-borne LiDAR point cloud data is an important data source for producing DEM. In order to further improve DEM production efficiency,selecting flat terrain and mountainous terrain as test areas,the ground point cloud,which is processed by filtering method,is thinned and simplified according to the algorithm based on TIN with seven different the ground point cloud retention rate of 80%,60%,40%,and so on,and the corresponding DEM is generated and its accuracy is evaluated by mean error (ME),standard deviation (SD),and root mean square error (RMSE). The results show that: ①The accuracy of the produced 0.5 m grid-spacing DEM could exceed 0.05 m when the ground point cloud density reached 2 points/m² for flat terrain and 9 points/m² for mountainous terrain. ②As the density of ground point cloud increases,the DEM accuracy level gradually stabilizes,and the DEM accuracy would decrease rapidly when the ground point cloud density is thinned to 1 point/m². For the DEM production tasks in large regions using UAV-borne LiDAR point cloud data,the conclusions of this research have a certain guiding and reference significance in reducing data acquisition costs and improving DEM production efficiency.

Studying the spatio-temporal characteristics and influencing factors of pollen allergy network attention is beneficial for mastering relevant information and serving people's livelihoods. This paper combines Baidu index,meteorological,and remote sensing data to analyze the spatiotemporal characteristics and influencing factors of pollen allergy network attention in eight urban agglomerations from 2017 to 2021. Random forest and back propagation neural network models are used for simulation. The results show that: ①In terms of spatiotemporal characteristics,the annual peak of attention is in spring (April and May). Cities with concentrated populations receive the highest attention. The city is spatially concentrated,with the Beijing-Tianjin-Hebei,Shandong Peninsula,Yangtze River Delta,and Pearl River Delta urban agglomerations as hot spots. ②In terms of relationship with influencing factors,within the effective range,with the increase of temperature,short term thunderstorm rainstorm,poor air quality,increased fraction of absorbed photosynthetically active radiation,and strong nighttime light,attention increases,and with high humidity,high wind speed,heavy rain,and prolonged rain,attention decreases. Attention has the highest correlation with nighttime light.③In terms of urban agglomerations,the temperature of the northern urban agglomerations,the humidity of the coastal urban agglomerations,the wind speed of the urban agglomerations with large undulating terrain,the precipitation of the southern urban agglomerations,the air quality index of the Pearl River Delta and the Yangtze River Delta and the fraction of absorbed photosynthetically active radiation of the Pearl River Delta and the Beijing-Tianjin-Hebei urban agglomerations are of great importance,and the nighttime light in each urban agglomeration is irreplaceable. ④Random forest and back propagation neural network are suitable for simulating attention,with R² ranging from 0.64 to 0.92 for each urban agglomeration,and RMSE and MAE below 1. Back propagation neural network has better simulation effects than random forest. The fit of both models in the Chengdu-Chongqing urban agglomeration is excellent,followed by the Beijing-Tianjin-Hebei,Pearl River Delta,and the middle reaches of the Yangtze River urban agglomeration. The methods and results of this paper can provide reference for work related to pollen allergy.

In view of the rapid urbanization in Hexi area of Nanjing and the few researches on settlement prediction in this area, this paper proposes a monitoring and prediction model of urban surface deformation based on small baseline subsets-interferometric synthetic aperture radar (SBAS-InSAR) and moving average-particle swarm optimization-backpropagation(MA-PSO-BP) neural network algorithm. The settlement monitoring of the Hexi area of Nanjing is carried out by using the 22 Sentinel-1A lift rail data from March 2020 to March 2022, the variable of the lifting rail in the study area is obtained, the trend and the causes of settlement in Hexi are analyzed, and the settlement value obtained is used as the sample input of the PSO-BP network model to construct a network prediction model. The results show that SBAS-InSAR technology can effectively monitor the long-term settlement of the city, there are different degrees of settlement in Hexi area of Nanjing, the settlement rate is -25.3～20.5 mm/a, compared with the historical settlement study, the settlement trend expands from north to south, combined with the settlement monitoring data of SBAS-InSAR, compared with BP neural network and PSO-BP neural network prediction model, the accuracy of the settlement prediction model after interpolation of sample data is the highest.

This paper selects four indices,including greenness (NDMVI),wetness (Wet),rocky desertification (RDI),and heat (LST),to construct an improved remote sensing ecological index (IRSEI) to evaluate the ecological environment quality of county karst areas. Based on county-scale remote sensing images and other geographical information data from 2003 and 2023,IRSEI and RSEI are quantitatively compared in several aspects. The results show that IRSEI contains richer information on mountain vegetation and rocky desertification conditions and can better characterize and evaluate the ecological environment quality of karst areas. And IRSEI is suitable as a quantitative indicator for the ecological environment quality evaluation of karst areas. In the study area of Luoping County,both improvement and degradation of ecological environment quality occurred between 2003 and 2023,but the overall ecological environment quality dropped by 7.46%,and the ecological grade changed from good to medium. From the perspective of spatial distribution,the areas with poor ecological quality in karst areas of Luoping County are mainly distributed in the north,and the areas with excellent ecological environment quality are mainly distributed in the central part.

Glacial change is an important embodiment of climate change and has a profound impact on global water resources and ecological environment. Based on Landsat series of remote sensing images, the paper extracted the glacier boundary in recent 30 years and analyzed the glacier area change characteristics of Dongkemadi River from 1990 to 2020. At the same time, the ice temperature was reversed based on atmospheric correction method, so as to explore the response relationship between glacier change and climate change. The results show that: ①From 1990 to 2020, the area of Dongkemadi glacier continued to decline, the total area shrank by 1 km², and the average annual change rate was -0.21%. ②Dongkemadi glaciers are widely distributed at the altitude of ［5600,5800)m, with the strongest glacier retreat at the altitude of ［5300,5400)m. In the past 30 years, the large and small winter and glaciers have retreated 382.89 m and 141.39 m respectively.③From 2017 to 2020, the surface deformation rate of Dongkma bottom glacier was between -233.192 and 172.512 mm·a^-1. The ice surface in the high altitude area was accumulated, and the ice tongue and some areas showed a thinning trend.④The area with high surface temperature of the glacier is mainly distributed in the western edge to the southern edge of the Great Dongkemadi Glacier and the western edge and part of the eastern edge of the Little Dongkemadi Glacier, and the lower temperature area is mainly distributed in the whole eastern part of the glacier. Rising temperatures may be the main factor leading to the melting of the glacier.

Aiming at the problem that the traditional optimal estimation alignment algorithm (OBA) can not be applied to low-cost INS, this paper proposes an improved OBA algorithm combined with extended Kalman filtering, which realizes the estimation of attitude error with the assistance of GNSS and can be applied to low-cost SINS system. Firstly, the reference vector and observation vector are reconstructed, and the accumulation of bias is suppressed by a fixed integration interval. Secondly, by establishing the correlation between the bias of the gyroscope and the attitude error, a new system state equation is constructed, and the measurement equation is constructed based on the position and velocity of GNSS output. At the same time, the bias and attitude error of the gyro are estimated, and the attitude error is fed back into the construction of multiple vectors, and finally are verified in the simulation experiment and the actual sports car experiment. The experimental results show that compared with the traditional OBA algorithm, the algorithm can realize fast alignment of the moving base, estimate the bias of the gyro and compensate the misalignment angle, improve the alignment accuracy when the carrier is in a long-term motion state, and improve the convergence speed without using sliding window integration. Compared with the FIMA algorithm, the accuracy of the three attitude angles is improved by 47%, 47% and 51%, respectively.

In pursuit of investigating pedestrian indoor positioning methods based on smartphones and enhancing the precision of indoor pedestrian localization, this paper proposes a localization system utilizing Wi-Fi RTT and IMU for the indoor positioning of pedestrians using smartphones. The method comprises three key components: ①The introduction of a Wi-Fi RTT indoor positioning method that employs extended Kalman filtering to integrate distance measurement information.②The proposition of a dead reckoning method suitable for multi-phone usage, utilizing LSTM to establish a neural network model for predicting pedestrian movement speed and heading.③The development of a fusion positioning method based on ESKF that combines Wi-Fi RTT and data-driven inertial navigation to further elevate positioning accuracy. Experimental findings illustrate that, in comparison to individual Wi-Fi RTT and data-driven inertial navigation methods, the proposed approach achieves an average improvement of 10% to 20% in positioning accuracy.

The “Metro Tunnel Safety Hidden Danger Comprehensive Inspection Vehicle” offers quick and accurate inspection of subway tunnel linings and potential hazards. Traditional positioning technologies in tunnels face issues like low accuracy and data sync problems due to complex underground and structural environments. To address this, a new on-board fusion positioning system with an accuracy correction algorithm has been developed. This system includes precise coded data and image acquisition, a fusion positioning algorithm, an accuracy correction algorithm, and a multi-source information coordinate conversion matrix.This fusion positioning system is more accurate than existing methods, capable of fast and dynamic positioning in both straight and curved tunnel sections. It achieves a positioning accuracy of 10 cm, with a relative error of less than or equal to 0.06% over up to 300 m in straight and 100 m in curved sections. The system's effectiveness and stability are confirmed by indoor and outdoor tests, making it a valuable tool for rapidly detecting and locating hidden safety hazards in subway tunnels, thereby enhancing tunnel safety.

Due to the fact that most of the cracks do not have distinctive features and are affected by cable,scratches,cobwebs and other linear interferences inside the tunnels,the detection effect of the existing crack detection methods,using high-resolution images,still needs to be improved. This paper takes the lining cracks as the research object,realizes the non-destructive data acquisition of the tunnel surface information based on the tunnel camera system,and acquires the high-resolution image data of 4096×2168 pixels. And we clarify the interference factors of crack identification,and constructs the interference data set and the real texture data set based on the characteristics of the disease; takes the Mask R-CNN model as the baseline framework,and adopts the K-means and genetic algorithm to optimize the parameters of RPN network. The detection effect and performance of this paper's algorithm are illustrated using comparative and ablation experiments. The results show that the algorithm proposed in this paper can realize the recognition and length measurement of tunnel cracks under high-resolution images,with lower probability of leakage and false detection,and has better detection performance for the slender and less obvious cracks,and the measured values of the cracks can provide reference information for the operation and maintenance of the subway.

Extreme rainstorm events and insufficient drainage facilities are very likely to lead to urban rain flood. The construction of large-scale rainwater pipe culverts based on the surrounding environmental safety assessment is an important measure for urban rain flood prevention. This paper takes the newly built rainwater pipe culvert project of Kefeng bridge in Fengtai district, Beijing city as the object, introduces risk chain theory, and uses fuzzy synthesis and MIDAS/GTS numerical simulation method to evaluate the surrounding land use risk during the construction period of the project. Research has shown that: ① During the construction period of the project, the risk impact levels of different land types around the project are different, and the deformation of the surrounding soil is concentrated within the range of 0～18 m outside the project, with 0~4 m being the high-risk area for deformation; ② The land deformation within a range of 80 m around the intersection of this project and urban underground pipelines shows an upward trend. The area between 0～12 m at the intersection is a high-risk area for uplift, and key monitoring and warning are required. In conclusion,the research results of the paper have reference value for quality monitoring of similar engineering projects and risk prevention of surrounding land use in China.

Water leakage always occurs in shield tunnels during operations that may result in cracks and corrosion of rebars. Such defects could jeopardize the safety of tunnel operations. Water leakage detection is thus necessary to ensure safe tunnel operations. This study proposes a method using 3D laser scanning technology for identifying locations of water leakages in shield tunnels. Firstly,the proposed method collects point cloud data for generating a grayscale image of the tunnel surface based on the corrected reflection intensity values. Then,dilation and erosion algorithms are used for preprocessing of the imagery data. Next,the authors use connected domain algorithm to derive locations and area of water leakage. Finally,the authors have validated the proposed method through a case study. Results show that the proposed method could achieve 92% accuracy for water leakage detection in shield tunnels.

The tense land use environment and the complex main structure demand in the city lead to the more and more common special-shaped foundation pit. The analysis methods recommended by the current domestic foundation pit specifications can not satisfy the influence of shape on the deformation of foundation pit. Based on a large special-shaped foundation pit in Beijing,the Midas-GTS simulation model of special-shaped foundation pit is established,and the deformation characteristics of surface settlement and envelope structure of special-shaped foundation pit are studied in combination with the deformation data measured on site. The results show that the surface settlement and the deformation of the supporting structure have obvious spatial effects under the influence of the shape characteristics of the foundation pit: the surface uneven settlement is significant,the settlement near the long side of the foundation pit is greater than the short side,and the settlement increases significantly with the increase of the distance from the pit angle. The deformation of the middle area of the long side of the wall is larger,followed by the middle of the short side,and the deformation of the corner of the structure is the least. So when designing and supporting foundation pit,the space effect of foundation pit deformation should be fully considered to save project cost and improve the overall stability of foundation pit.

Time series InSAR technology is an effective means to monitor surface deformation. Based on 179 Sentinel-1A image data,PS-InSAR technology is used to monitor and analyze the surface deformation along the Cishousi station to Lucheng station of Beijing subway Line 6,and the typical settlement interval is analyzed in detail. At the same time,the monitoring results are compared and analyzed and the accuracy is evaluated by using the ground level data.At the same time,the accuracy of monitoring results is evaluated using ground leveling data. The results show that there are different degrees of settlement along Beijing subway Line 6,and the average annual settlement rate is different from west to east. The average annual settlement rate in the west of Jintailu station is small,but it increases rapidly from Jintailu station to east,and the maximum average annual settlement rate is -32 mm/a. The tilt value to the east of Jintailu station is large,and the tilt value fluctuates obviously,which leads to uneven settlement.There is a strong correlation between leveling results and PS-InSAR monitoring results. The average error between them is 3.5 mm and the median error is 4.7 mm,which shows that PS-InSAR has certain reliability and accuracy.

This paper combines 3D laser scanning technology with BIM technology to realize 3D modeling of underground stations, tunnels and ancillary facilities of urban rail transit. The point cloud data is obtained by laser scanner, and the point cloud processing software is used for pre-processing, including splicing, denoising and simplifying operations. Then, the feature point cloud extraction method combined with BIM modeling software and visualization programming technology, which is used to effectively solve the problem that traditional surveying and mapping methods can not obtain and process complex underground building information and information island in BIM model. This method is of great significance for the future development of urban rail transit engineering construction and operation and makes maintenance management towards informatization and three-dimensional visualization.

This paper addresses the characteristics of domestic satellite imagery,such as multi-temporal,long-time series,massive,and massive multi-source data,proposes an efficient and accurate method for the extraction of satellite imagery patches. Based on the principles of deep learning,this method constructs a semantic segmentation model for ground objects and a group of intelligent algorithms for patch extraction based on deep learning theory,enabling the automatic recognition of the features,patterns,and attributes of satellite imagery patches,which leads to the intelligent and automated extraction of these patches. Experimental results demonstrate that this method achieves a high level of accuracy in the extraction of patches from domestic satellite imagery,provides important support for subsequent image processing,analysis,and applications.

In order to better apply NeRF high-precision 3D modeling in the 3D digital reconstruction of urban real scenes,this paper divides the large scene into sub-NeRF based on NeRF technology,and initializes the polygon mesh by constructing multiple octahedral bodies in the scene. And the vertices of the polygon faces are continuously optimized during the training process. After the training is completed,the weights of the encoder-decoder network are obtained,and different levels of polygon mesh refinement are performed on each independent block through vertex optimization. From satellite-level images that capture city overviews to ground-level images that show complex details of buildings,multi-scale data for urban detail and spatial coverage are constructed through progressive learning. The neural network voxel rendering model uses a multilayer perceptron (MLP) to realize the parameterization of volume density and color,and uses a hierarchical sampling method to realize the sorting distance vector of rays between the near plane and the far plane of a predefined viewing angle,so as to realize real-time interactive rendering of large-scale scenes. Then,GIS and NeRF are fused to provide a new solution for tasks such as multi-data fusion,query,analysis,measurement,annotation and sharing,so as to achieve instant and smooth dragging,zooming and 360° browsing and viewing of scenes without dead ends. This fusion makes it easy to integrate various data sources for spatial analysis in 3D scenarios such as urban planning,land management and environmental monitoring.

Quantitative analysis of the driving force of spatio-temporal evolution of land urbanization can provide scientific basis for sustainable urban development.This paper combines earth observation data with population and economic data,takes 8 cities along the Yangtze River economic belt (Jiangsu section) as examples,and based on 5 Landsat images from 2000 to 2020,night light images,total population and GDP.GeDetector is used to discuss the evolution of spatial,population and economic dimensions and the process of multi-scale differentiation in the past 20 years,and analyze the spatial-temporal evolution law and driving factors of land urbanization.The results show that: ①The urbanization process directly drives the change of land surface type,and the population and GDP can be used as quantitative indicators of the urbanization process intensity.②NTL data coupled with population or economic statistics can better explain the spatio-temporal differences in the process of regional urbanization.③The urbanization process of 8 cities along the Yangtze River economic belt (Jiangsu section) has experienced a change from quantity to quality.The research results provide scientific basis for guiding the rational allocation of urban construction land,and provide decision-making reference for urban sustainable development.

Integrated multiple lens cameras have been adopted for aerial photogrammetry in the industries in recent years thanks to its high data-acquisition efficiency. However, the cameras yield redundant photos for non-target areas which causes extra computation power. This issue becomes serious when the shape of the target area gets complicated. In this paper, we propose a novel screening method for the redundant aerial photos based on optical geometry and building recognition algorithms. The method first project the photos onto target area, and the overlaps are estimated. Combining the building recognition results with the estimated overlap ratios, the redundant photos can be identified and removed. The results suggest that the proposed method is able to reduce 70% of the captured photos but keep almost the same accuracy for reconstruction of the target area.

Tianditu is an important carrier for government departments to provide common geographic information services. It integrates massive geographic information resources and provides users with authoritative, standard, and unified online geographic information services. In the new era, with the development of economy, society, and technology, various fields have put forward higher requirements for common geographic information services. Therefore, by analyzing the situation faced by the construction of Tianditu, this paper proposes that the construction of Tianditu in the new era needs to focus on solving the problemof accurate coupling among the service objects, service content, and service methods, and focus on promoting the continuous evolution of the construction model and overall structure of the common geographic information service platform under new technological conditions, continue to improve the service model of geographic information resources from discrete, offline to integrated, and online, and establish a nationally integrated geographic information resource opening and sharing system.

With the widespread application of high-definition map data usage is increasingly popular in the autonomous driving industry. This paper proposes a logical algorithm based on different road scene features to quickly identify road intersection topology connection problems for high-precision map road intersections applications in the autonomous driving industry. By using the internal connection algorithm of road intersections, various scenarios of missing connections at road intersections can be identified, resulting in effective recognition and protection for most scenarios. Experimental results based on multiple city-map data show that the logical algorithm proposed in this article is both rapid and effectively for identifying topological connection problems within road intersections, This algorithm also preventively identifies topological connection breakpoints caused by topological connection mismatch between laboratory simulation and realistic vehicle testing. The research shows promising applications on both map quality inspection, simulation, testing and other processes in the field of autonomous driving, through reducing the number of autonomous driving downgrades and improving user experience. The experimental results provides opportunities for further improvement on autonomous driving technology by solving road intersection problems and refining vehicle perception.

This article designs a solution for intelligent information services in 15-minute radius livelihood service circles. In response to the current problems in the informatization construction of 15-minute radius livelihood service circles, in-depth research and key design have been conducted on the overall framework, information construction standards, information collection platforms, information expression methods, and information application models. Taking Caoyangxincun Subdistrict as an example, demonstration practice has been carried out to enhance the data, technical, and management capabilities of intelligent information construction in 15-minute radius livelihood service circles, and to lay the foundation for promoting the construction of 15-minute radius livelihood service circles nationwide.

With the widespread use of navigation maps and electronic maps,traditional maps still demonstrate advantages in terms of user spatial cognition due to their fixed scale. Based on relevant theories and research in the field of geographic spatial cognition,this article proposes the following methods for the design and compilation of government maps,based on an analysis of the application of provincial government maps:①Adhere to the principles of map synthesis and design map elements,expressions,graphical design,and annotations with targeted approaches.②Implement basic methods for expressing geographical spatial divisions and map level divisions to meet the requirements of visualizing geographical elements in government map design.③Optimization of map data structure based on hierarchical representation of cartographic elements to meet the needs of rapid configuration in map compilation,and to meet the needs of multi-scale mapping for fast mapping or single mapping scale adaptive spatial expression.

In the digital age, the refined management of megacity behemoth requires deepening the application of modern information technologies such as the internet, big data, and artificial intelligence. By utilizing precision and intelligence as the driving force, it promotes the integration of urban management resources and processes re-engineering through digital empowerment. In this article, firstly, the concept of urban intelligent pan-info surveying and mapping is proposed in combination with the pilot work of the new basic surveying and mapping in Shanghai. Moreover, its key technologies are introduced in five aspects, including “pan-info data acquisition with multi-sensor fusion, pan-info data fusion and intelligent processing, geographic entity database construction, geographic knowledge service and multi-mechanism triggered data updating”. Finally, the application results are exhibited in various fields of society such as the unified management of urban governance, district-level urban operation platform, and street-level digital twin.

In order to address the problems that the relevant knowledge points are abstract and difficult to understand, as well as the disconnection between theory and practice in the teaching process of BeiDou navigation and positioning, this paper aims to improve the quality of education and students' comprehensive abilities, takes students as the center, follows the law from theoretical learning to practical application, and constructs a whole-process, systematic and progressive Beidou navigation and positioning virtual simulation teaching method integrating “cognition-exploration-application” with the help of virtual reality technology. The method covers a series of teaching contents, including the principles of Beidou Navigation system, system composition, navigation positioning, range measurement error and elimination, and high-precision digital mapping and applications of RTK. This teaching method can simulate the national Beidou instrument, cultivate students' patriotism, break the limitations of time and space, stimulate students' innovation ability, promote the integration of theory and practice, and improve the quality of education. It is very necessary to cultivate surveying and mapping engineering professionals who are proficient in the knowledge of Beidou Navigation system navigation and positioning and have relevant practical skills.

Big data brings new opportunities and challenges for college professional innovation education in the new era. Taking the geographic information science specialty as an example, this paper systematically analyzed and summarized the current situation of college innovation education in our country and the main challenges it is facing. We made an exploration and practice for the reform of innovation education for geographic information science specialty in the big data era, and proposed new reform measures based on the permeation of innovation idea, the raising of innovation awareness, the training of innovation thinking and the evaluation of innovation ability. These measures can provide references for the comprehensive construction of college innovation education system.